Data Science News Flash: 09-19-2019
The latest Data Science articles - algorithmically curated, ranked, and summarized just for you.
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- But before buying AI data storage, an organization must consider a range of requirements based on how data is acquired, processed and retained by machine learning platforms.
- I've already highlighted the need to collect, store and process large volumes of data to create machine learning or AI models.
- Machine learning and AI algorithms process data in parallel, running multiple tasks that can read the same data multiple times and across many parallel tasks.
- Running machine learning tools in the public cloud reduces the capital cost of building infrastructure for machine learning development, while offering the ability to scale infrastructure needed to develop machine learning models.
- Building AI data storage can be hard, as additional factors must be considered for storage networking and tuning storage to work with machine learning applications.
- Machine learning is a predictive algorithm where input data will be provided and Machine learning will provide output results via automatic learning.
- Machine learning and Deep Learning are often misunderstood as a revolutionary method, while it remains a sophisticated data analysis method.
- Doctors, who use automatic learning systems, can better learn about their construction, the data sets on which they are built and their limitations.
- Conversely, properly deployed automatic learning systems can help to address disparities in health care delivery by compensating for known biases or identifying areas where more research is needed to balance the underlying data.
- With the ever-increasing number of publications in the biomedical field, researchers are increasingly turning to AI to analyze this data and identify promising areas of research.
- Anxieties about artificial intelligence begat Jack Williamson’s “With Folded Hands,” William Gibson’s Neuromancer, Alex Garland’s Ex Machina, and Jonathan Nolan and Lisa Joy’s “Westworld”.
- Some have warned that advances in AI could erode the fundamental logic of nuclear deterrence by enabling counter-force attacks against heretofore concealed and mobile nuclear forces.
- While emerging technologies and nuclear force postures might interact to alter the dynamics of strategic competition, AI in itself will not diminish the deterrent value of today’s nuclear forces.
- Poor data and technological constraints limit AI’s impact on the fundamental logic of nuclear deterrence, as well as on other problem sets requiring near-perfect levels of confidence.
- While faulty paradigms sustain misplaced expectations about AI’s impact, poor data and technological constraints curtail its effect on the fundamental logic of nuclear deterrence.
- The amount of data available for Data Science tasks is usually many magnitudes larger than sample sizes used in most research.
- Most statistical tests assume normal distribution as a result of the central limit theorem, Machine Learning models can work with all kinds of distributions.
- However, most Machine Learning models rely on random number generators when building models, splitting data or generating random distributions.
- This means that the very same types of Machine Learning models (even Neural Networks) could have slightly different outputs when trained on the very same data.
- Machine Learning models generally lack transparency, could yield different results when trained multiple times on the very same data, will most likely learn some biases from the dataset, and need to be constantly maintained and updated with new data in order to remain useful.
- Dr Scott Zoldi, chief analytics officer at analytic software firm FICO, explains how data science cowboys are exacerbating the the AI and analytics challenge.
- Are data science cowboys and citizen data scientists the greatest hinderance to your AI and analytics ambitions?
- In the below, Dr Scott Zoldi, chief analytics officer at analytic software firm FICO, explains to Information Age why data science cowboys and citizen data scientists could cause catastrophic failures to a business’ AI and analytics ambitions.
- The former does not have data science training but uses analytic tooling and methods to bring analytics into their businesses; the latter has data science training, but a disregard for the right way to handle AI.
- Today’s AI threat stems from the efforts of both citizen data scientists and data scientist cowboys to tame complex machine learning algorithms for business outcomes.
- Therefore, it is hardly a surprise that health care is among the areas that are mostly affected by the newest technologies, such as Artificial Intelligence and Big Data.
- However, the projection is that according to the latest trends in the world of medicine, we are steadily moving towards employing Artificial Intelligence, “deep learning” in particular.
- There are numerous applications of Artificial Intelligence in health care, but all the potential uses that people can benefit from are best comprehended if categorized in three distinct types.
- One of the advantages of Artificial Intelligence and machine learning, in general, is that it learns as it goes.
- Artificial Intelligence has the potential to deliver tools that would take care of the day-to-day medical practice and would, therefore, spare doctors from carrying out mundane tasks.
- Today, the terms artificial intelligence (AI) and machine learning (ML) are often used interchangeably.
- Active learning is the philosophy that “a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns”.
- Human labelers are required for any sort of ML, but with Active Learning their work is significantly reduced by the machine selecting the most relevant data.
- Active learning leverages readily available, and often imperfect, AI to actively select new data that it believes would be most beneficial when developing the next, improved version of the AI.
- Active Learning therefore can significantly reduce the amount of data required to develop a performant AI system because it only learns from the most relevant data.
- In Britain, France and the European Union, government agencies examine the ethical, social and economic impact of artificial intelligence and other big new technologies used in health care and elsewhere.
- The White House’s Office of Science and Technology Policy created an AI task force in 2018, but its concern was promoting U.S. competitiveness, not oversight.
- Just as ELSI studies helped ease the introduction of genetic technology into medicine, they could improve understanding of the risks and benefits of genetically modified foods, digital health records—and AI, notes Brody.
- The General Accountability Office created a Science, Technology Assessment and Analytics team in January, but GAO’s interactions with Congress are formal and its reports can take months.
- Back in the day, members of Congress could call the Office of Technology Assessment on the phone for clarification of difficult technological issues, said Rep. Bill Foster (D-Ill.), a physicist who chairs the Finance Committee’s Task Force on Artificial Intelligence.
- Oh, the wonderous things artificial intelligence, machine learning, deep learning, big data analytics, and the correlations and causations from data science are uncovering these days!
- Data monetization is a growing big data analytics business, anticipated to reach over $200 billion by 2020.
- However, a lack of accuracy and lack of standard of handling of such data means those responsible for the collection, storage, analysis and destruction of this data are balancing on a slippery slope of ethical decisions.
- It’s important to bear in mind that, when facial recognition data is collected, ethical data storage decisions must be made.
- For more perspectives on unlocking the value of data with artificial intelligence systems, explore Dell Technologies AI Solutions and Dell EMC Ready Solutions for AI.
- The editors at Solutions Review have perused the 2019 Forbes AI 50 and identified these top data analytics firms as warranting extra attention.
- DataRobot offers an automated machine learning platform for data scientists of all skill levels to build and deploy accurate machine learning models.
- DataRobot searches through millions of combinations of algorithms, data pre-processing steps, transformations, features, and tuning parameters to spit out the best model for your data.
- Domino Data Lab is an enterprise data science platform that allows data scientists to build and run predictive models.
- The technology was originally invented by Dr. Michael Stonebraker and his colleagues who published their research about the Data Tamer System for handling large-scale data curation in 2013.
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