AI News, Deep Learning And Artificial Intelligence (AI) Solutions
Artificial Intelligence (AI) vs. Machine Learning vs. Deep Learning
You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out.
John McCarthy, widely recognized as one of the godfathers of AI, defined it as “the science and engineering of making intelligent machines.” Here are a few other definitions of artificial intelligence: There are a lot of ways to simulate human intelligence, and some methods are more intelligent than others.
The intelligence that rules engines mimic could be that of an accountant with knowledge of the tax code, who takes information you feed it, runs the information through a set of static rules, and gives your the amount of taxes you owe as a result.
Usually, when a computer program designed by AI researchers actually succeeds at something – like winning at chess – many people say it’s “not really intelligent”, because the algorithm’s internals are well understood.
–Tom Mitchell In 1959, Arthur Samuel, one of the pioneers of machine learning, defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” That is, machine-learning programs have not been explicitly entered into a computer, like the if-then statements above.
This has three names: an error function, a loss function, or an objective function, because the algorithm has an objective… When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?
Deep artificial neural networks are a set of algorithms that have set new records in accuracy for many important problems, such as image recognition, sound recognition, recommender systems, natural language processing etc.
For example, deep learning is part of DeepMind’s well-known AlphaGo algorithm, which beat the former world champion Lee Sedol at Go in early 2016, and the current world champion Ke Jie in early 2017.
So you could apply the same definition to deep learning that Arthur Samuel did to machine learning – a “field of study that gives computers the ability to learn without being explicitly programmed” – while adding that it tends to result in higher accuracy, require more hardware or training time, and perform exceptionally well on machine perception tasks that involved unstructured data such as blobs of pixels or text.
Are we chasing a breakthrough like nuclear fission (possible), or are attempts to wring intelligence from silicon more like trying to turn lead into gold?1 There are four main schools of thought, or churches of belief if you will, that group together how people talk about AI.
In 1404, during the reign of Henry IV, the English parliament passed a law called the “Act against multiplication”, outlawing the creation of gold and silver from other materials, since that was seen as a threat to the throne’s control over the currency.
Artificial intelligence in healthcare
Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data.
What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user.
AI algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can't adjust itself and only understand what it has been told explicitly, (2) and algorithms are black boxes;
AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care.
to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs.
that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.
During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians.
The ability to interpret imaging results with radiology may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss.
A study at Stanford created an algorithm that could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial.
The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can achieve improvements in certain statistical metrics in isolated cases, as opposed to specialists.
Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.
In 2018, a paper published in the journal Annals of Oncology mentioned that skin cancer could be detected more accurately by an artificial intelligence system (which used a deep learning convolutional neural network) than by dermatologists.
On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN machine.
One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline.
To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature.
Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.
Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports.
The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility.
A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.
Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.
team associated with the University of Arizona and backed by BPU Holdings began collaborating on a practical tool to monitor anxiety and delirium in hospital patients, particularly those with Dementia.
The AI utilized in the new technology – Senior's Virtual Assistant – goes a step beyond and is programmed to simulate and understand human emotions (artificial emotional intelligence).
Doctors working on the project have suggested that in addition to judging emotional states, the application can be used to provide companionship to patients in the form of small talk, soothing music, and even lighting adjustments to control anxiety.
Virtual nursing assistants are predicted to become more common and these will use AI to answer patient's questions and help reduce unnecessary hospital visits.
Overall, as Quan-Haase (2018) says, technology “extends to the accomplishment of societal goals, including higher levels of security, better means of communication over time and space, improved health care, and increased autonomy” (p. 43).
While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues.
We already have some scientists who know artificial intelligence and machine learning, but we want complementary people who can look forward and see how this technology will evolve.”
As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.
Principal Specialist Sales, Artificial Intelligence (AI), Machine Learning (ML).
The ideal candidate will possess a proven track record and background that enables them to lead a sales effort that will include teaming with AWS Senior Enterprise Sales Representatives, Solutions Architects, Partners and engagements at the CxO level within the largest AWS accounts.The successful candidate will also possess a business background that enables them to drive an engagement and interact at the CxO/VP level, as well as a technical understanding that enables them to easily interact with developers, technical architects, and data scientists.
You should also have a demonstrated ability to think strategically and analytically about business, product, and technical challenges, with the ability to build and convey compelling value propositions, and work cross-organizationally to build consensus.Responsibilities:· Drive revenue and market share for AWS AI/ ML services· Meet or exceed quarterly and annual revenue targets.· Develop and execute against a sales plan.· Maintain a robust sales pipeline.· Work with partners to extend reach & drive adoption of the AI/ ML solutions.· Manage contract negotiations along with the core sales organization.· Develop long-term strategic relationships expand use of AWS AI/ML across all business units.· Ensure customer satisfaction.· Expect moderate travel.
- On 14. april 2021
Artificial Intelligence and Machine Learning Solution
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