The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.
As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.
Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate . Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity .
Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these technologies work in practice and when (and when not) to use them is often not clear to practitioners. In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis. We also offer guidance on which of these are ready for use, their strengths and weaknesses, and how a systematic review team might go about using them in practice.
Although software tools that support the data synthesis component of reviews have long existed (especially for performing meta-analysis), methods for automating this are beyond the capabilities of currently available ML and NLP tools. Nonetheless, research into these areas continues rapidly, and computational methods may allow new forms of synthesis unachievable manually, particularly around visualization [37, 38] and automatic summarization [39, 40] of large volumes of research evidence.
The torrential volume of unstructured published evidence has rendered existing (rigorous, but manual) approaches to evidence synthesis increasingly costly and impractical. Consequently, researchers have developed methods that aim to semi-automate different steps of the evidence synthesis pipeline via machine learning. This remains an important research direction and has the potential to dramatically reduce the time required to produce standard evidence synthesis products.
Most of the tools we encountered were written by academic groups involved in research into evidence synthesis and machine learning. Very often, these groups have produced prototype software to demonstrate a method. However, such prototypes do not age well: we commonly encountered broken web links, difficult to understand and slow user interfaces, and server errors.
a, Suggested mechanism for the synthesis of compound 19. b, Small library of compounds synthesized. c, Suggested mechanism for the synthesis of compound 22. d, Suggested mechanism for the synthesis of compound 21.
Automation of the moleculardesign-make-test-analyze cycle speeds up the identification of hit and leadcompounds for drug discovery. Using deep learning forcomputational molecular design and a customized microfluidics platform foron-chip compound synthesis, liver X receptor(LXR) agonists were generated from scratch. The computational pipeline wastuned to explore the chemical space defined by known LXRα agonists, and to suggeststructural analogs of known ligands and novel molecular cores. To further thedesign of lead-like molecules and ensure compatibility with automated on-chip synthesis,this chemical space was confined to the set of virtual products obtainable from17 different one-step reactions. Overall, 25 de novo generated compoundswere successfully synthesized in flow via formation of sulfonamide, amide bond,and ester bond. First-pass in vitro activity screening of the crudereaction products in hybrid Gal4 reporter gene assays revealed 17 (68%) hits, withup to 60-fold LXR activation. The batch re-synthesis, purification, andre-testing of 14 of these compounds confirmed that 12 of them were potent LXRα orLXRβ agonists. These results support theutilization of the proposed design-make-test-analyze framework as a blueprint forautomated drug design with artificial intelligence and miniaturized bench-topsynthesis.
Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas.
Key ML terms and principles may be found in Table 1. Many of the ML applications discussed in this article rely on deep neural networks, a subtype of ML in which interactions between multiple (sometimes many) hidden layers of the mathematical model enable complex, high-dimensional tasks, such as natural language processing, optical character recognition, and unsupervised learning. In January 2020, a diverse group of stakeholders, including leading biomedical and ML researchers, along with representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies convened in Washington, DC, to discuss the role of ML in clinical research. In the setting of relatively scarce published data about ML application to clinical research, the attendees at this meeting offered significant personal, institutional, corporate, and regulatory experience pertaining to ML for clinical research. Attendees gave presentations in their areas of expertise, and effort was made to invite talks covering the entire spectrum of clinical research with presenters from multiple stakeholder groups for each topic. Subjects about which presentations were elicited in advance were intentionally broad and included current and planned applications of ML to clinical research, guidelines for the successful integration of ML into clinical research, and approaches to overcoming the barriers to implementation. Regular discussion periods generated additional areas of interest and concern and were moderated jointly by experts in ML, clinical research, and patient care. During the discussion periods, attendees focused on current issues in ML, including data biases, logistics of prospective validation, and the ethical issues associated with machines making decisions in a research context. This article provides a summary of the conference proceedings, outlining ways in which ML is currently being used for various clinical research applications in addition to possible future opportunities. It was generated through a collaborative writing process in which drafts were iterated through continued debate about unresolved issues from the conference itself. For many of the topics covered, no consensus about best practices was reached, and a diversity of opinions is conveyed in those instances. This article also serves as a call for collaboration between clinical researchers, ML experts, and other stakeholders from academia and industry in order to overcome the significant remaining barriers to its use, helping ML in clinical research to best serve all stakeholders.
Interpretation of large amounts of highly dimensional data generated during in vitro translational research (including benchtop biological, chemical, and biochemical investigation) informs the choice of certain next steps over others, but this process of interpretation and integration is complex and prone to bias and error. Aspuru-Guzik has led several successful efforts to use experimental output as input for autonomous ML-powered laboratories, integrating ML into the planning, interpretation, and synthesis phases of drug development [8, 9]. More recently, products of ML-enabled drug development have approached human testing. For example, an obsessive-compulsive personality disorder drug purportedly developed using AI-based methods is scheduled to begin phase I trials this year. The lay press reports that the drug was selected from among only 250 candidates and developed in only 12 months compared with the 2000+ candidates and nearly five years of development more typically required . However, due to the lack of peer-reviewed publications about the development of this drug, the details of its development cannot be confirmed or leveraged for future work. 2b1af7f3a8