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Artificial Intelligence is Revolutionizing Cancer Research
Published in K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc, Machine Learning and Deep Learning Techniques for Medical Science, 2022
B. Sudha, K. Suganya, K. Swathi, S. Sumathi
Artificial intelligence (AI) is one of the most common subjects of modern science. It is an evolving field that focuses on research and improvement in human intelligence modeling, extension, expansion hypotheses, processes, technology, and implementation systems. AI is a subdivision at its core, which is the science of computing (Bi et al. 2019; Abbasi, 2018). It is a broad phrase that includes machine learning and deep learning. Machine learning is an area of AI that focuses on deep artificial neural networks, while deep learning is a virtualization technology that concentrates on deep artificial neural networks. Due to its unique performance in computer vision tasks, such as face recognition and image categorization, among others, machine learning has acquired excellent output in recent years (LeCun, Bengio and Hinton, 2015).
Computer-Aided Epitope Identification and Design of Epitope Mimetics
Published in Mesut Karahan, Synthetic Peptide Vaccine Models, 2021
The experience with IEDB provokes several observations regarding the practicality of the available state-of-the-art B cell epitope prediction tools. First, the algorithms deliver reasonably accurate predictions, although a competent structural biologist could readily reach similar conclusions without the use of bioinformatics tools. Second, the algorithm’s advantage over human intelligence is even more disputable considering that the predictions are not fully reliable, and the results require a critical evaluation by a human researcher anyway. Third and foremost, these predictions are the end of the road within established science. The identified epitope sequences alone are generally not suitable to the role of vaccine components.
Understanding Difference
Published in Al Condeluci, Beyond Difference, 2020
It is also important to know that every element of the human experience has limits or parameters of acceptance and rejection. Consider human intelligence. Although suspect, we do have measures that offer some gauge to intelligence. These tests are routinely given and render scores that rate people. Administered over thousands of children of similar age, these test scores are divided at various cutoff levels. A high, low, and median are established and then individual scores are rated off these intervals. Children are then said to be slow, gifted, or average based on where they fall in test scores. The slow or gifted children are clearly perceived and then treated by the school district as different. Indeed, most of the offerings within the typical school curriculum are designed for the average student. This often forces the slow or gifted students out of the school district to special schools if they are going to grow in the educational experience.
Artificial Consciousness Is Morally Irrelevant
Published in AJOB Neuroscience, 2023
This restrictive regulatory approach is impractical. Some morally relevant aspects of consciousness are crucial requirements for technology such as self-driving vehicles and robots. They will need to make moral judgments so they can interact safely with us, and consequently research into artificial moral agents is forging ahead (Cervantes et al 2020). Further, AI is rapidly advancing, and recent developments such as ChatGPT have highlighted its vast potential. Researchers are unlikely to be easily dissuaded from attempting to develop AI that simulates or exceeds human intelligence, given the rewards of doing so. As much research is driven by the private sector, regulation will be difficult to approve and enforce. There are also legal difficulties in enforcing these regulations, as there is no way to determine if a machine is actually conscious, or merely cleverly programmed to act conscious. Instead, we should act on the assumption that we will eventually develop conscious machines, whether we are aware of it or not. This raises several issues.
Proposal for a method for analysing smart personal protective systems
Published in International Journal of Occupational Safety and Ergonomics, 2022
Patrice Marchal, James Baudoin
The concept of ‘smartness’ or ‘intelligence’ is more complex to define, such is the variety of fields concerned with it (psychology, engineering, computer science and information technology, etc.). In the context of this study, we looked for definitions proposed for defining either technical systems or smart materials. It is on these aspects that most of the ‘smart’ PPE identified in the previously established state of the art is based. For the French dictionary Larousse, the term ‘intelligent’ (i.e., ‘smart’ in English) as applied to machinery defines equipment that is maintained or operated by an automated device capable of replacing human intelligence for certain operations. Similar definitions are to be found in English dictionaries: for Webster’s, ‘smart’ can mean operating by automation (when applied to machinery), and using a built-in microprocessor for automatic operation, for processing of data or for achieving greater versatility (when applied, e.g., to a card); while for the Oxford learner’s dictionary, as applied to a device, ‘smart’ means controlled by a computer so that it appears to act in an intelligent way.
What Exactly “History Has Taught us”? Enhancing the Socio-Political Perspective in Neuroethics
Published in AJOB Neuroscience, 2022
Marcelo de Araujo, Murilo Vilaça
The authors’ reliance on a “Rawlsian framework” may fail to do justice to the increasing recognition that societal perception of human enhancement varies greatly across the international socio-political spectrum, as shown, for instance, by the SIENNA Project, which we followed closely as one of its members. The SIENNA Project employed the CATI method (Computer Assisted Telephone Interviewing) to conduct interviews in 11 countries across different economic, cultural, and geographical landscapes, namely: France, Germany, Greece, the Netherlands, Poland, Spain, Sweden, Brazil, South Africa, South Korea, and the United States. In each country, one thousand individuals were asked about their perceptions of recent developments in artificial intelligence, genomics, and human enhancement technologies. Take for example “Support for technology to make people more intelligent”. The number of respondents that declared they “strongly support” the development of cognitive enhancement technologies varied greatly from country to country: South Africa (46%), Brazil (26%), Spain (22%), USA (19%), Greece (19%), South Korea (17%), Poland (17%), Sweden (10%), Netherlands (9%), Germany (7%), France (7%) (Prudhomme 2020, 30). A similar pattern emerged with respect to “Acceptability of conducting laboratory experiments on human embryos to understand how to increase human intelligence” (Hanson 2020, 45; Araujo 2020, 13–14).