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Why To Choose Python Development In 2020?

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Recently, Python has become the most chosen language for data science and Artificial intelligence--two technological innovation patterns for worldwide organizations to remain competitive in today's era. Truth be told, Python is the quickest developing programming language today, as indicated by Stack overflow's 2019 developer Survey. Known for its meaningfulness and flexibility, every organization, regardless of its size, is using this language. New businesses may upgrade a small design group's workflow by using Python's proficient syntax structure and utilizing its many package libraries. Big organizations may go to python to process mammoth datasets utilizing Artificial Intelligence algorithms.


IIT Roorkee joins Coursera to launch 2 AI, ML programmes - Express Computer

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The Indian Institute of Technology-Roorkee (IIT-R) in partnership with leading online learning platform Coursera on Thursday launched two new online certificate programmes for professionals looking to build skills in data science, Artificial Intelligence (AI) and Machine Learning (ML). The six-month certificate programme in AI and ML will consist of video lectures, hands-on learning opportunities, team projects, tutorials and workshops. The programme will also teach classical ML techniques and provide hands-on programming experience with'Tensorflow' software for model building, robust ML production and powerful experimentation. The certificate programme in data science will help professionals build skills in data science, machine learning, critical thinking, data collection, data visualization and data management. "We are delighted to partner with Coursera to help fulfil the goal of inclusive education of the New Education Policy," Professor Ajit K Chaturvedi, Director, IIT Roorkee, said in a statement.


Benzinga - Application

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Samurai is an innovative provider of trading solutions, novel quantamental research, alternative data, and specialized risk/hedging tools. By leveraging Artificial Intelligence (AI) and Machine Learning (ML) with our team's niche experience in market structure analysis and volatility research, Samurai's unique solutions empower our clients to better define risks, identify opportunities, and most importantly, generate outsized returns. We've developed our solutions from the ground up with wealth managers, traders and market participants in mind. With multiple latency options available, a highly scalable infrastructure, and seamless integration, Samurai is flexible and easily deployable in any environment. Engineered by a sophisticated combination of proprietary methodology and niche industry expertise, our clients benefit from decreased volatility, lower market correlations and unmatched results.


Artificial intelligence examines best ways to keep parolees from recommitting crimes - ScienceBlog.com

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Starting a new life is difficult for criminals transitioning from prison back to regular society. To help those individuals, Purdue University Polytechnic Institute researchers are using artificial intelligence to uncover risky behaviors which could then help identify when early intervention opportunities could be beneficial. Results of a U.S. Department of Justice study indicated more than 80 percent of people in state prisons were arrested at least once in the nine years following their release. Almost half of those arrests came in the first year following release. Marcus Rogers and Umit Karabiyik of Purdue Polytechnic's Department of Computer and Information Technology, are leading an ongoing project focused on using AI-enabled tools and technology to reduce the recidivism rates for convicted criminals who have been released.


Gartner Market Guide for AIOps Platforms – BMC Blogs

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Like Gartner, who report a 25% increase in end user inquiries on AIOps, we at BMC Software are also experiencing increased interest from customers who are challenged by increasing complexity and volumes of data which are beyond human scale to manage. The combination of big data, machine learning, analytics and automation is increasingly being recognized amongst IT leaders as having the potential to transform monitoring and event management and drive significant benefits across IT Operations processes. So, what are Gartner saying are the major changes in this updated 2019 Market Guide for AIOps?


Gartner Identifies Top 10 Data and Analytics Technology Trends for 2020

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Gartner, Inc. identified the top 10 data and analytics (D&A) technology trends for 2020 that can help data and analytics leaders navigate their COVID-19 response and recovery and prepare for a post-pandemic reset. "To innovate their way beyond a post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts," said Rita Sallam, distinguished research vice president at Gartner. By the end of 2024, 75% of organizations will shift from piloting to operationalizing artificial intelligence (AI), driving a 5 times increase in streaming data and analytics infrastructures. Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. Other smarter AI techniques such as reinforcement learning and distributed learning are creating more adaptable and flexible systems to handle complex business situations; for example, agent-based systems that model and simulate complex systems.


New machine learning tool predicts devastating intestinal disease in premature infants

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Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease of prematurity. Characterized by sudden and progressive intestinal inflammation and tissue death, it affects up to 11,000 premature infants in the United States annually, and 15-30% of affected babies die from NEC. Survivors often face long-term intestinal and neurodevelopmental complications. Researchers from Columbia Engineering and the University of Pittsburgh have developed a sensitive and specific early warning system for predicting NEC in premature infants before the disease occurs. The prototype predicts NEC accurately and early, using stool microbiome features combined with clinical and demographic information. The pilot study was presented virtually on July 23 at ACM CHIL 2020.


Daily AI Roundup: The 5 Coolest Things On Earth Today

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AI Daily Roundup starts today! We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities in artificial intelligence, Machine Learning, Robotic Process Automation, Fintech and human-system interactions. We will cover the role of AI Daily Roundup and their application in various industries and daily lives. Bayard Bradford developed the Ultimate Data Export app as a result of participating in HubSpot's new App Accelerator program.


Discovery of aggressive cancer cell types made possible with machine learning techniques

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By applying unsupervised and automated machine learning techniques to the analysis of millions of cancer cells, Rebecca Ihrie and Jonathan Irish, both associate professors of cell and developmental biology, have identified new cancer cell types in brain tumors. Machine learning is a series of computer algorithms that can identify patterns within enormous quantities of data and get'smarter' with more experience. This finding holds the promise of enabling researchers to better understand and target these cell types for research and therapeutics for glioblastoma--an aggressive brain tumor with high mortality--as well as the broader applicability of machine learning to cancer research. With their collaborators, Ihrie and Irish developed Risk Assessment Population IDentification (RAPID), an open-source machine learning algorithm that revealed coordinated patterns of protein expression and modification associated with survival outcomes. The article, "Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells" was published online in the journal eLife on June 23.


Artificial Intelligence and Archives • CLIR

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—Rebecca Bayeck and Azure Stewart “Artificial Intelligence and Archives” was the inaugural webinar of the series on Emerging Technologies, Big Data & Archives, organized by CLIR postdocs Rebecca Y. Bayeck of the Schomburg Center for Research in Black Culture and Azure Stewart of New York University. With the emergence of new technologies and big data, the processing and preservation of data has changed and will continue to change. As in other domains (e.g., health, video games), artificial intelligence (AI) is increasingly reshaping the way we process, interact with, and think about archives. Consequently, in the age of big data, archives are not just “a collection of historical records relating to a place, organization, or family” (Cambridge Dictionary Online). Today, archives also include all types of digital data—including social media data—and algorithms. Archivists are therefore called on to preserve and process data as they are being created, which requires understanding AI languages, processes, and practices for the creation and protection of data/records now for the future. In this webinar, our speaker Dr. Anthea Seles, from the International Council on Archives (ICA), discussed AI in archival spaces: its uses, application, and the role archivists should play to become critical voices in AI discussions. Two hours were not enough to address all the questions raised by the 280 attendees. As a follow up to the webinar, we have thematically organized and addressed the unanswered questions and present them here. Artificial Intelligence in Archives How much has AI penetrated archives in the developing world? I would say [this has been] limited, if at all. I think the main issue is that these technologies are being applied in the assessment of development initiatives like Sustainable Development Goals (SDGs). Increasingly there are many projects focusing on artificial intelligence and human rights, for example the University of Essex Human Rights, Big Data and Technology Project, and it is becoming a concern for organisations like Amnesty International. Who already has the best AI for archives today, according to ICA regulation, that we can adopt? There is no commercial provider that works specifically on archival questions. I think you can use off-the-shelf eDiscovery software, but you need to have a basic understanding of what the technology is doing in order to measure your precision and recall.  Artificial Intelligence Tools Will governments and big corporations use artificial intelligence as a tool to centralize information in future? Potentially. I think there is some thinking about this coming out of the records management community, but I still believe it is about balancing the strengths of the tool with the continuing need for human intervention. The question is, when will the human be needed? And what can the tool be trusted to do with minimum supervision? How do we ensure a continuous feedback loop to identify records of long-term value as information creation changes?  What tools were you using for the file analysis and visualization in this presentation? The screen shots are only example photos, they are not from any of the tools we used. We looked at several eDiscovery tools with different algorithms (e.g., Latent Semantic Indexing, Latent Dirichlet Allocation). These are bog standard machine learning applications that have been around for a while, and we chose to go down that road to see what we could get in off-the-shelf commercial software packages. So, is there a way to write a script to avoid metadata corruption and alteration? There are tools now you can use that will preserve the integrity of the metadata when you move material from one system or file to another. I think for historical metadata alteration/corruption it is a question of how we explain this to users and how this might affect different access methods like visualisation.  Will the International Council on Archives provide training on artificial intelligence and machine learning? Not yet, but I’m open to suggestions. [We are] currently speaking with different stakeholders and maybe we can hold a hackathon at the Abu Dhabi Congress.  Access to Archives Will the course Managing Digital Archives be accessible online? The managing digital archives course is organized by the ICA and will be accessible online in fall 2020. Please check the ICA website or social media channels (Twitter and Facebook) for more information. What are some of the practices in the UK National Archives and government on managing structured data as records? How does the UK identify, capture, manage, and apply retention and disposition to data (both transactional applications and analytical ones)? There are no published policies on identification of datasets that I can see and would suggest you contact either the record copying or the UK government web archive records unit to see if anything more substantive has been developed. What is your suggestion for keeping physical records for posterity and authentication? Records should always be maintained in the format in which they are created. The belief in scanning paper records and destroying them in order to save space and save on storage costs is a false economy. The level at which you should be scanning that material and the amount of metadata that should be captured to maintain it over time is very high. Also, you need to take into account computer storage costs, and whether you can afford the costs of digital preservation software, which all begins to add up. One must also take into account the active management of these authentic digital surrogates by digital preservation specialists. Furthermore, if you have a paper management problem and you don’t take that into account when you move into the digital environment you are then transferring an analog integrity issue into a digital integrity/authenticity issue. Digital will not solve integrity issues; in my opinion it will magnify them. Artificial Intelligence and Society In Brazil, we are concerned with the problem of the spread and political use of misinformation (fake news). How can archivists with algorithm training provide reliable research insights to fight against this historical problem? At this point, I couldn’t honestly provide you with an answer but Read More