Research
Human-Computer Interaction (HCI)
Human-Computer Interaction (HCI) integrates concepts and methods from computer science, design, and psychology to build interfaces that are accessible, easy to use, and efficient.
In the early 1960s, at a time where computers were scarce, expensive, bulky and formal-scheduled machines used for automatic calculations, Douglas Engelbart saw their potential as personal interactive tools. He initiated a research program aimed at developing computing hardware and software to augment the human intellect, to increase a person’s capabilities to approach a complex situation, to gain comprehension to suit his or her particular needs, and to derive solutions to problems. At the Stanford Research Institute, he and his colleagues created the first system realizing this vision. Their oN-Line System (NLS) was not only designed to augment the capabilities of its users, but also to foster their collaboration. Among other firsts, its 1968 demonstration featured the introduction of document processing, hypermedia, shared files, messaging, real-time distant collaboration, multiple windows and the computer mouse. NLS was way ahead of its time. It impressed and inspired many people. Few realized the vision behind it, however. Due to complex hierarchies of modes and commands, it was also difficult to use and required substantial training. This difficulty with initial use and its reliance on networked time-shared computers were fatal to the system, and Engelbart’s vision of personal computing as an augmentation tool somewhat faded away.
Research in Human-Computer Interaction (HCI) has been spectacularly successful, and has fundamentally changed computing. Just one example is the ubiquitous graphical interface used by Microsoft Windows 95, which is based on the Macintosh, which is based on work at Xerox PARC, which in turn is based on early research at the Stanford Research Laboratory (now SRI) and at the Massachusetts Institute of Technology. Another example is that virtually all software written today employs user interface toolkits and interface builders, concepts which were developed first at universities. Even the spectacular growth of the World-Wide Web is a direct result of HCI research: applying hypertext technology to browsers allows one to traverse a link across the world with a click of the mouse. Interface improvements more than anything else has triggered this explosive growth. Furthermore, the research that will lead to the user interfaces for the computers of tomorrow is happening at universities and a few corporate research labs.
Most scholars and historians in the field of HCI agree the birth of the discipline was in the late 1970’s and early 1980’s, which coincides with the launch of the Personal Computer (PC). The origins in personal productivity interactions are bound to the desktop, so to speak. The discipline started as a specialty area in computer science with several research labs being started around the United States. Most of these research labs were at large research universities, with two of the larger being Stanford and MIT. Universities partnered with industry to create research labs as well with some notable lab collaborations being Xerox, IBM, and AT&T. During this time, Graphical User Interfaces were coming of age and the leading researcher in this arena was Xerox PARC labs.
It is clearly impossible to list every system and source but some are listed below:
Software Tools and Architectures
- UIMSs and Toolkits
- Interface Builders
- Component Architectures
Application Types
- Drawing programs
- Text Editing
- Spreadsheets
- HyperText
- Computer Aided Design (CAD)
- Video Games
Other Areas
- Gesture Recognition
- Multi-Media
- 3-D
- Virtual Reality and “Augmented Reality”
- Computer Supported Cooperative Work
- Natural language and speech
- Robotics
Basic Interactions
- Direct Manipulation of graphical objects
- The Mouse
- Windows
Jonathan Grudin is a Principal Design Researcher at Microsoft working in the fields of human-computer interaction (HCI) and computer-supported cooperative work (CSCW). Grudin is a pioneer of the field of CSCW and one of its most prolific contributors. His collaboration distance to other HCI researchers has been described by the Grudin number. Grudin is also well known for the Grudin Paradox or Grudin Problem, which states basically with respect to the design of collaborative software for organizational settings, “What may be in the managers’ best interests may not be in the interests of individual contributors, and therefore not used.” He was awarded the inaugural CSCW Lasting Impact Award in 2014 on the basis of this work. He has also written about the publication culture and history of HCI. His book From Tool to Partner, The Evolution of Human-Computer Interaction was published in 2017.
Three key areas composed the discipline at the advent of HCI. They were: Computer Science, Cognitive Psychology, and Design. These areas have expanded in the past decades to include: Human Factors Engineering, Industrial Design, Interaction Design, Branding, Anthropology, Technical Communications, Market Research, Software Testing, and Training to name a few. The growth of the discipline has been far reaching, and as a result has attracted many disciplines to participate in the research. The field of HCI has far reaching research, combines several disciplines, and has enriched every theory and method it has appropriated. The roles of the UX practitioner have their foundations in HCI. The two are inseparable.
The original and abiding technical focus of HCI was, and is, the concept of usability. Current roles in HCI emerge in the UX sector such as Experience Researcher, Industrial Designer, Human Factors Specialist, Usability Engineer, Information Architect, and the UX Researcher. These positions all fall within the spectrum of HCI practitioner.
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Health
Artificial intelligence (AI) and machine learning (ML) in the field of cancer treatment
Especially in the areas of diagnosis and treatment, artificial intelligence (AI) and machine learning (ML) have the potential to change the field of cancer research. AI and ML are able to find patterns and insights in vast amounts of data that may not be immediately obvious to human researchers.
Image analysis is one use of AI and ML in the field of cancer research. AI can analyse medical pictures like X-rays, CT scans, and MRIs to find malignant tumours and other anomalies by utilising deep learning algorithms. This might result in earlier cancer detection and help doctors make more accurate diagnoses.
By facilitating the analysis of enormous amounts of data, artificial intelligence (AI) has the potential to transform the area of cancer research by enabling the analysis of vast amounts of data, speeding up the discovery of new therapies, and improving patient outcomes. There are several ways in which AI is being used in cancer research, including:
- Image analysis: AI algorithms can be used to analyze medical images, such as X-rays and CT scans, to identify signs of cancer and monitor its progression. This can help to diagnose cancer at an early stage and track its response to treatment.
- Drug discovery: AI can be used to analyze large amounts of data to identify new targets for drug development and to optimize the design of drugs to maximize their efficacy and minimize side effects.
- Predictive analytics: AI algorithms can be trained on large datasets to predict patient outcomes and to identify patients who are most likely to respond to a particular therapy. This information can be used to personalize treatment plans and improve patient outcomes.
- Clinical trial design: AI can be used to analyze patient data and identify patients who are most likely to participate in clinical trials, which can speed up the development of new therapies.
There have been several successful case studies of AI in cancer research, including the development of new drugs for the treatment of lung and breast cancer, as well as the development of algorithms for early cancer detection and personalized treatment planning.
AI and ML are being used in cancer research for drug discovery and development. AI can analyze large amounts of data, such as genetic and protein information, to identify potential drug targets and predict how different compounds will interact with the body. This can help speed up the drug development process and increase the chances of success. AI and ML are also being used to analyze patient data, including medical records, imaging, and genomics data, to identify patterns and insights that can help in the personalized treatment of cancer. This can help doctors make more informed treatment decisions and improve patient outcomes.
It’s important to note that AI and ML in cancer research are still in their early stages and there are still many challenges to be overcome. These include the need for large amounts of high-quality data to train the models, the need for robust validation methods and the need to address ethical and legal issues. AI and ML have the potential to revolutionize cancer research, particularly in the areas of diagnosis and treatment. They can be used for image analysis, drug discovery and development, and personalized treatment. However, more research is needed to overcome the challenges and ensure that these technologies can be used safely, ethically, and effectively in the fight against cancer.
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Editor's Picks
Antineoplaston can be used as an alternative treatment for cancer – part 2/2
Read part 1 before continuing part 2
Antineoplastons are a group of naturally occurring peptides and amino acid derivatives that have been proposed as a treatment for cancer. The theory behind antineoplaston therapy is that these compounds can selectively target and kill cancer cells while leaving healthy cells unharmed.
Antineoplaston therapy was first developed by Dr. Stanislaw Burzynski in the 1970s. Dr. Burzynski discovered the compounds while studying peptides in blood and urine, and he began using them to treat cancer patients in the 1980s. Over the years, Dr. Burzynski and his team have conducted multiple clinical trials to test the safety and efficacy of antineoplaston therapy.
The results of these trials have been mixed. Some patients have reported significant improvements in their cancer symptoms, while others have not seen any benefit. Additionally, some studies have suggested that antineoplaston therapy may have toxic side effects. The American Cancer Society (ACS) states that the safety and effectiveness of antineoplaston therapy have not been proven. The FDA has approved a limited number of clinical trials for antineoplastons for specific types of brain tumors, but larger and well-designed studies are still needed to confirm the safety and efficacy of antineoplaston therapy.
It’s worth noting that Antineoplaston therapy is not widely accepted in the medical community, and the scientific evidence supporting its use as a cancer treatment is limited. The FDA has not approved antineoplaston therapy as a treatment for cancer, and it is not widely available in the United States.
Antineoplaston therapy is an alternative cancer treatment that has been proposed as a treatment for cancer, but its safety and effectiveness have not been proven. There are still many questions about the long-term safety and efficacy of antineoplaston therapy, and more research is needed to determine whether it is a viable treatment option for cancer patients. Patients should consult with their healthcare providers before considering antineoplaston therapy as a cancer treatment. It’s important to remember that while alternative therapies may have some positive effects, they may not be as effective as standard cancer treatments and may have negative side effects.
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Editor's Picks
Antineoplaston can be used as an alternative treatment for cancer – part 1/2
Antineoplastons are a group of natural compounds that have been proposed as a treatment for cancer. The compounds were discovered by Dr. Stanislaw Burzynski, a Polish-American physician, who began studying them in the 1970s. Dr. Burzynski claims that antineoplastons can selectively target and kill cancer cells while leaving healthy cells intact.
Antineoplastons are made from substances that are found naturally in the body, such as amino acids and peptides. They are administered orally or intravenously. According to Dr. Burzynski, antineoplastons work by restoring the balance of genetic regulation in cancer cells, which leads to cancer cell death.
There have been several clinical trials of antineoplastons in patients with different types of cancer, including brain tumors, lung cancer, and breast cancer. Some of these trials have reported positive results, with patients showing improvement in their symptoms and tumor size reduction. However, many of the trials have been small and have not been conducted using rigorous scientific methods.
The scientific community has been divided in their opinion on the effectiveness of antineoplastons as a cancer treatment. Some researchers have criticized the lack of rigorous scientific evidence supporting the use of antineoplastons, while others have pointed out the promising results seen in some of the trials.
The U.S. Food and Drug Administration (FDA) has not approved antineoplastons as a cancer treatment. The FDA has instead approved a phase II clinical trial to evaluate the safety and effectiveness of antineoplastons in treating certain types of brain tumors.
It’s worth mentioning that antineoplastons as a cancer treatment is considered as alternative or experimental therapy, and its use should be discussed with a doctor before making any decision. Also, it’s important to note that the FDA has not approved antineoplastons as a cancer treatment, and their safety and effectiveness have not been established.
Antineoplastons are a group of natural compounds that have been proposed as a treatment for cancer. While there have been some reports of positive results in the treatment of cancer with antineoplastons, more rigorous scientific evidence is needed to establish their safety and effectiveness. It’s important to talk to a doctor before considering antineoplastons as a cancer treatment and to be aware that it is not approved by the FDA.
Here is the link for part 2
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