The value of AI might come not so much in making machines that act like humans, but stopping humans acting like machines.
M. du Sautoy, 2019
According to IBM[1], “Artificial Intelligence (AI) is a technology that enables computers and machines to simulate human intelligence and problem-solving capabilities. On its own or combined with other technologies (e.g., sensors, geolocation, robotics), AI can perform tasks that would otherwise require human intelligence or intervention”.
Manufacturing robots, self-driving cars, digital assistants, GPS guidance, healthcare management, automated financial investing, virtual travel booking agents, social media monitoring, marketing chatbots and generative AI tools (like Open AI’s Chat GPT) are just a few examples of AI.
As a field of computer science, AI uses machine learning, deep learning, natural language processing, computer vision, robotics, etc. AI algorithms perform tasks that typically require human intelligence, such as understanding natural language, recognising patterns, making decisions, and exhibiting creativity. Today, AI has applications across numerous domains, including healthcare, finance, transportation, education, entertainment, and much more, in industry, government, and scientific research. Notable applications include search engines like Google Search; recommendation systems utilised by platforms such as YouTube, Amazon, and Netflix; voice-activated assistants like Google Assistant, Siri, and Alexa; creative tools such as ChatGPT and AI-generated art; playing better-than-humans strategy games like chess and Go.
- AI today: Spectacular results
Developing diagnostic tools for skin cancer is very important in curing it. A recent study published in Nature[2] compared the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. It identified 2,983 studies, of which 10 were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8%, and specificity was 81.5%; for AI-assisted clinicians, the overall sensitivity was 81.1%, and the specificity was 86.1%. AI-assisted diagnostics benefitted medical professionals of all experience levels in subgroup analyses, with the most significant improvement among non-dermatologists. No publication bias was detected, and the findings were robust.
Similar results have been reported for reading mammograms. An article published in the August 2023 issue of The Lancet Oncology[3] reports that with the help of AI software, 20% more cancers have been found than two radiologists’ routine, double reading and no increase in false positives were recorded. […]
[1] Articol dedicat Prof. Lila Kari, cu ocazia implinirii unei vârste rotunde.
https://www.ibm.com/topics/artificial-intelligence
[2] https://www.nature.com/articles/s41746-024-01031-w
[3] https://tinyurl.com/4k42crvz