Dalla protezione delle balene alla maggiore comodità, cosa non può fare l’IA?

L’intelligenza artificiale è sempre più presente nella vita quotidiana. L’introduzione degli LLM (modelli linguistici di grandi dimensioni), come ChatGPT, ha messo l’IA alla portata di tutti. Tuttavia, la tecnologia era già sulla buona strada per cambiare la vita delle persone molto prima che gli LLM arrivassero sul mercato. Ecco uno sguardo a ciò che l’IA non è ancora in grado di fare e a dove potrebbe arrivare in futuro.

Evoluzione dell’IA

The AI sector has undergone numerous iterations based on the technology’s capabilities and accessibility. The concept of human-like machines dates back to ancient times. The Renaissance revived these concepts, and famous inventors like Leonardo da Vinci demonstrated the concept with automatons in 1495.

L’intelligenza artificiale come la vediamo oggi può essere fatta risalire alla Macchina Universale di Alan Turing del 1935. Solo 15 anni dopo, creò il test di Turing. Il test di Turing utilizza la conversazione umana per determinare se un sistema IA ha raggiunto capacità simili a quelle umane e oltre. Questo test è ancora usato oggi come punto di riferimento per le interazioni IA.

The 1990s computational breakthroughs helped to push AI systems further. Machine learning and other AI systems began to emerge during this time, leading to breakthroughs in the early 2000s. For example, IBM demonstrated Watson’s AI skills by beating out humans in a game of jeopardy. At this same time, Google started releasing data on its neural network systems.

IA Oggi

This decade has seen some of the greatest leaps in AI technology so far. The introduction of self-learning AI technologies like torque clustering algorithms has made the sector a point of discussion.

These technological gains have been met with moral questions and environmental concerns as well. All of these factors have led to more scrutiny of the future of AI and its influence on daily lives. So what can’t AI do? These projects highlight just some of its current capabilities and limitations.

Aiutare le persone a costruire città migliori

I sistemi di IA possono aiutare gli urbanisti a creare comunità più funzionali ed efficienti. Possono monitorare il traffico e altri dati essenziali, molto utili per stabilire dove realizzare nuovi servizi, autostrade e altre infrastrutture necessarie alla comunità.

Approfondimenti sulle infrastrutture

I sistemi di IA vengono già utilizzati per rendere i quartieri più sicuri e confortevoli ricavando indicazioni utili dai dati. Uno studio¹ pubblicato sul Journal of Smart Cities and Society analizza in modo approfondito la sicurezza e la stabilità delle reti elettriche.

Specifically, it examines energy distribution, house requirements, what type of energy source each house uses, and if that places the location at a higher risk of power loss. The study explains that as more houses move towards all electric options, some risks may have been overlooked.

In particular, the report highlights how solar panels are a great source of energy during the summer, but how they can leave entire communities without power during winter storms and other environmental conditions. Sadly this scenario has become more common as the push for all electrified homes continues.

Costruire un modello IA per classificare il rischio di perdita di energia

Stevens researchers analyzed data from the Department of Energy (DOE) building stock, including the energy patterns of 129,000 single-family homes. These houses were located in eight different states, allowing the team to test the system in various environments.

The ML models successfully determined individual house energy system footprints. It then processed this data, cross-referenced it with its data models, and determined what type of energy sources the house relied on.

Furthermore, the system took this data and used it to decide the blackout risks to the house and community. Now, the team seeks to expand its testing to more communities to help planners build safer and more resilient electrical grids.

Salvare la Terra

Environmental conditions, pollution, human expansion, and other factors have left many wildlife regions in tatters. Artificial intelligence has been one solution that environmentalists have turned to to help determine wildlife patterns.

These systems can help conservationists notice sudden changes in species, which is a valuable tool that can assist in protecting rare and endangered animals from extinction. Already, AI systems can track migration patterns, health, poacher activity, and other vital data.

Conservazione delle balene

A team of innovative researchers from Rutgers University-New Brunswick introduced an modello IA designed to help international shipping vessels avoid endangered species populations recently.

Gli autori principali dello studio², Ahmed Aziz Ezzat e Josh Kohut, hanno creato il modello di migrazione e monitoraggio delle balene utilizzando diversi tipi di dati per proteggere la rara balena franca nordatlantica. Purtroppo, i dati della National Oceanic and Atmospheric Administration indicano che in natura ne restano meno di 400. Della popolazione rimasta, soltanto 70 esemplari sono femmine in età riproduttiva, un dato che accresce la preoccupazione degli ambientalisti.

Fonte – Nature Communications

Specifically, the team integrated the Rutgers University Center for Ocean Observing Leadership data from as far back as the early 1990s, satellite imagery from the University of Delaware, and underwater glider info into the model. Notably, gliders are underwater craft that have an array of sensors. They traverse the ocean floor helping to map uncharted regions, track environmental changes, and search for valuable resources,

The AI predicted the location and time of whale populations based on the whale’s preferences, past locations, environmental conditions, and time of year. It enabled researchers to connect these dots and find the patterns that increased whale sighting possibilities. In the future, this system will accurately show ships where whale populations are, enabling them to rechart a course avoiding these natural habitats.

Intelligenza Artificiale delle Cose (AIoT).

The melding of Artificial Intelligence and Internet of Things technologies created the now budding AIoT industry. This sector combines the reach and data gathering capabilities of IoT systems with pattern recognition, processing, and convenience AI systems provide.

AIoT Systems enable logistics companies to get their products across the globe faster, reduce counterfeiting, and provide a host of other high-level features that would be impossible. AIoT improves on the IoT concept in a couple of key ways including efficiency.

Traditional IoT systems operate as sensors. They gather and send data via the internet to another location that then processes that data.  AIoT eliminates the need to send the data over the internet. Instead, these systems can process the information in-house, reducing time, costs, and bandwidth requirements.

Economia intelligente

AIoT will serve a crucial role in tomorrow’s smart economy. Today’s smart homes rely on various human activity recognition protocols to try and provide convenience to their operators. For example, imagine telling Alexa that you want to work out, and initiating a sequence of tasks designed to make the process more convenient.

These tasks could include adjusting the lighting, playing music, closing the blinds, and even starting up the after-workout smoothie maker. All of this is possible now using preset macros. However, the use of advanced human activity recognition AI systems will automate this process.

MSF-Net

Incheon National University engineers recently raised eyebrows after presentato³ their MSF-Net (multiple spectrogram fusion network) Wifi human activity tracking system. The system can leverage channel state information (CSI) to determine subtle human activity.

This technology will make smart homes more convenient. Soon AI will be able to determine if you are cooking, resting, watching TV, or about to leave for work and adjust the environment accordingly. This technology could see integration into multiple sectors including manufacturing, security, and healthcare.

Consigli morali

Sembra che, per quanto possa diventare avanzata la tecnologia dell’IA, rimarrà sempre una certa diffidenza verso questi sistemi. Un recente studio⁴ pubblicato sulla rivista Cognition evidenzia che le persone non si fidano dei sistemi di IA quando si tratta di consigli morali.

The School of Psychology studies why there is a distrust of AI systems, how it has slowed adoption, and what it would take to overcome these roadblocks to adoption. The researchers looked at the rise of artificial moral advisors (AMAs) and how they could one day become common.

Artificial moral advisors are purpose-built algorithms that leverage pre-programmed ethical theories, principles, and guidelines to provide users with answers to difficult moral dilemmas.

Notably, the study found that people were wary of AI advice on moral issues, even when it was the same advice given by a human advisor. It also demonstrated that many people have misconceptions about AI and concerns regarding its ability to go rogue.

In the future, you may have an AMA by your side to help you make the right choice. At the very least, it will document and probably report any “wrong” choices you make. As such, it’s easy to see why humanity may be ready to give AI missile launch codes, but not let it run the sermon.

Cosa non può fare l’IA? Ce ne è ancora molto

The AI revolution is in full swing and major technological breakthroughs continue to drive AI system capabilities further. Despite the constant echo that AI is coming for your job, most people still have plenty of time before their robot overlords take control. However, the human touch is still a way out.

Many factors like distrust and misunderstandings will inevitably lead to skepticism regarding AI’s true capabilities and purpose. For now, this tech has infinite potential but remains limited in its ability to connect to humans. As LLMs and robotics improve, the line between humanity and AI may meld further, opening the door for new realities.

Learn about Other Cool AI Projects Ora

Riferimento allo studio:

^{1. Majowicz, A., Popli, C., & Odonkor, P. (2025). Quantificare la vulnerabilità domestica alle interruzioni di corrente: valutare i rischi della rapida elettrificazione nelle smart city. Journal of Smart Cities and Society, 0(0). https://doi.org/10.1177/27723577241306340}

^{2. Ji, J., Ramasamy, J., Nazzaro, L., Kohut, J., & Ezzat, A. A. (2024). Apprendimento automatico per modellare la presenza della balena grigia dell’Atlantico settentrionale per supportare lo sviluppo dell’energia eolica offshore negli Stati Uniti del Mid‑Atlantic. Scientific Reports, 14, 29147. https://doi.org/10.1038/s41598-024-80084-z}

^{3. Chen, J., Xu, X., Wang, T., Jeon, G., & Camacho, D. (2024). Un framework AIoT con fusione multimodale di frequenze per il riconoscimento di attività WiFi a livello grossolano e fine. IEEE Internet of Things Journal, 11(24), 39020-39029. https://doi.org/10.1109/JIOT.2024.3400773}

^{4. Myers, S., & Everett, J. A. C. (2025). Le persone si aspettano che i consulenti morali artificiali siano più utilitaristi e diffidano dei consulenti morali utilitaristi. Cognition, 256, 106028. https://doi.org/10.1016/j.cognition.2024.106028}