Microsoft recently described the ways in which its artificial intelligence (AI) and machine learning offerings, as well as its Azure cloud, are being used by researchers and other public health groups responding to the ongoing coronavirus (COVID-19) pandemic.

In one collaboration announced Friday, Adaptive Biotechnologies Corp. and Microsoft are using Azure to map the immune system’s response to threats, including COVID-19. Adaptive is using Azure’s machine learning capabilities to sift through data on the body’s T-cell receptor sequences, which get generated in response to antigens in the blood. The machine learning process is used to refine a “map” of those sequences, which is done by “matching trillions of T cells to the diseases they recognize,” per Adaptive’s fact sheet (PDF download).

Leveraging Microsoft’s hyperscale machine learning capabilities and the Azure cloud platform, the accuracy of the immune response signature will be continuously improved and updated online in real time as more trial samples are sequenced from the study,” the announcement stated.

The aim of the effort is to more accurately detect diseases via blood tests. The partnership between Microsoft and Adaptive on the T-cell antigen map isn’t new, but dates back to 2018. It’s already led to a proof-of-concept for identifying Lyme disease. Adaptive plans to apply for clinical trials with the U.S. Food and Drug Administration sometime this year.

Adaptive is promising to share T-cell response signatures from COVID-19 with other researchers via a soon-to-come “open data access portal.”

“These data will be made freely available to any researcher, public health official or organization around the world via an open data access portal,” the announcement stated regarding the COVID-19 data.

Adaptive plans to use de-identified blood samples from individuals diagnosed with COVID-19 to bolster its data samples, and is seeking additional contributors. It’s working with LabCorp on the blood collection effort. Adaptive also is currently collaborating with the Providence health group, whose hospital in Seattle “treated the first U.S. COVID-19 patient.”

COVID-19 Assessment Bot
In addition to these efforts, Microsoft announced on Friday that it is supporting the U.S. Centers for Disease Control and Prevention’s (CDC’s) newly released COVID-19 assessment bot, which is “powered by Microsoft Azure.” The bot, which asks users health questions to assess possible COVID-19 infections, is based on Microsoft’s Healthcare Bot service and uses artificial intelligence in the screening process.

“Microsoft’s Healthcare Bot service is one solution that uses artificial intelligence (AI) to help the CDC and other frontline organizations respond to these inquiries, freeing up doctors, nurses, administrators and other healthcare professionals to provide critical care to those who need it,” the announcement explained.

Hospital systems are already using Microsoft’s bot to screen COVID-19. They include Seattle-based Providence, Virginia Mason Health System in the Pacific Northwest and Novant Health in the Southeast. Customized versions of the bot are currently handling “more than 1 million messages per day.”

Microsoft is helping other organizations build their own health bots by providing four “COVID-29 response templates.” Two of the templates are based on the CDC’s protocols. Organizations can modify the templates, if wanted.

COVID-19 Portal
The CDC reports U.S. cases of COVID-19 at this page, which includes a U.S. “heat map” illustration of the disease’s distribution. Cases have been reported in all 50 states.

Another site showing COVID-19 distribution around the world is nCoV2019.live. The site was put together by Avi Schiffmann, a high school junior from Mercer Island near Seattle, according to a report by the Democracy Now! news program. Schiffmann used Web scraping to pull the data together from various government sites.

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Data Science has demonstrated to be a boom to both the IT and the business. The innovation incorporates getting value from information, understanding the information and its examples and afterwards foreseeing or producing results from it. Data science is much popular by organizations to analyze their enormous volume of data sets and generate optimized business insights from them, in this manner expanding profits for the organization.

Picking the correct seller and solution can be an entangled procedure, one that requires in-depth research and regularly boils down to something other than the solution and its technical abilities. To make your hunt somewhat simpler, we’ve profiled the best data science platforms and tools.

Altair

Altair Knowledge Works (some time ago Datawatch) offers an advanced data mining and predictive analytics workbench called Knowledge Studio. The product includes licensed Decision Trees, Strategy Trees, and a work process and wizard-driven graphical UI. It additionally incorporates capacities for data preparation tasks, visual data profiling, advanced predictive modeling, and in-database analytics. Users can import and export using common languages like R and Python, as well as data types like SAS, RDBMS, CSV, Excel, and SPSS.

Mozenda

Mozenda is an enterprise cloud-based web-scraping platform. It assists organizations with gathering and sorting out web information most productively and cost-effectively possible. The tools have a point-to-click interface with an easy to understand UI. The tools have two sections: an application to construct the data extraction project and Web Console to run agents, organize results, and export data. It is anything but difficult to incorporate and permits users to distribute results in CSV, TSV, XML, or JSON format. The tools additionally give API access to get information and have inbuilt storage integrations like FTP, Amazon S3, Dropbox, and much more.

Anaconda

Anaconda is an open-source Python and R data science platform. The tool empowers you to perform data science and machine learning on Linux, Windows, and Mac OS. The platform permits users to download in excess of 1,500 Python and R data science packages, oversee libraries, dependencies, and environments, and analyze data with Dask, NumPy, pandas, and Numba. You would then be able to imagine results produced in Anaconda with Matplotlib, Bokeh, Datashader, and Holoviews.

Octoparse

Octoparse is a customer- side web scraping programming for Windows. It is a web-scraping template that transforms unstructured or semi-structured information from sites into an organized data set without coding. It is helpful for individuals who are not knowledgeable about programming. A web scraping layout is a simple yet amazing element. Its motivation is to input the target website/keywords in the parameters on the pre-formatted tasks, so the user doesn’t need to design any scraping rules nor composing code.

Databricks

Databricks offers a cloud and Apache Spark-based brought together analytics platform that joins data engineering and data science functionality. The platform uses a variety of open source languages and incorporates exclusive highlights for operationalization, performance and real-time enablement on Amazon Web Services. A Data Science Workspace empowers users to explore data and build models collaboratively. It additionally gives single click access to preconfigured ML conditions for augmented machine learning with popular frameworks.

OnBase

OnBase is a tool created by Hyland, is a single enterprise information platform that is intended to deal with user’s content, procedures, and cases. The tool essentially brings together user’s business content in a protected area and afterwards conveys important data to the user when they need it. OnBase permits the enterprise to turn out to be progressively agile, efficient, and capable, subsequently increasing productivity, delivering excellent customer service, and reduce risk across their enterprise.

KNIME Analytics Platform

KNIME makes understanding the information and designing data science workflows and reusable components available to everybody by being natural, open, and ceaselessly integrating new developments. KNIME permits the user to browse 2000 nodes to build workflow, model each step of the analysis, control the flow of data, and ensures the work is updated. The product likewise mixes tools from various areas with KNIME native nodes within a single workflow, incorporating scripting in machine learning, Python or R, or connectors to Apache Spark.

Dataiku

Dataiku offers an advanced analytics solution that permits companies to make their own data tools. The organization’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified structure for advancement and deployment gives prompt access to all the features expected to plan data tools without any preparation. Users would then be able to apply machine learning and data science systems to build and deploy predictive data flows.

Rapid Miner

Fast Miner is a data science platform developed fundamentally for non-programmers and analysts for quick analysis of information. The user has a thought in their brain, and effectively makes processes, import data into them, run them over and throw a prediction model. The tool supports importing ML models as well as to web applications like flask or nodeJS, android, iOS, and more, thereby unifying the entire spectrum of the Big Data Analytics Lifecycle.

DataRobot

DataRobot offers an enterprise AI platform that automates the end-to-end process for building, deploying, and maintaining AI. The product is controlled by open-source algorithms and can be utilized on-prem, in the cloud or as a completely overseen AI service. DataRobot incorporates three independent yet fully integrated tools (Automated Machine Learning, Automated Time Series, MLOps), and each can be deployed in different manners to coordinate business needs and IT necessities.

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3i Infotech Limited, a global Information Technology company, launched AMLOCK Analytics, its advanced anti-money laundering (AML) solution powered by Artificial Intelligence (AI) and Machine Language (ML), which enables banks and financial institutions to identify complex and hidden AML patterns. It helps organizations to meet their most critical challenge of managing high false positives and provides a holistic view of investigating an alert. AMLOCK Analytics uses various statistical methods and machine learning algorithms to derive analyses and predictions based on institution specific historical data.

One of the important features of 3i Infotech’s AMLOCK Analytics is the reduction of false positives using risk profiling, through predictive analytics that identifies potential risk and thereby enhances decision making. The solution also provides an insight on the trends followed by customers, based on seasonality and identifies the anomalies based on deviation from these trends, where machine learning helps in customer segmentation. This enables users to investigate effectively by working closely on those groups which are risky or deemed outliers.

Ravikanth Sama, Global Head- AML Practice, 3i Infotech said, “AMLOCK Analytics blends both the traditional rule-based system and the power of Analytics to bring better efficiency & risk focus.  It can be hosted both on-premise and on cloud infrastructure. The solution provides a probability score indicating the chances of closing an alert based on the past actions taken by the users on similar alerts. AMLOCK Analytics improves the conversion rate of Suspicious Transaction Report (STR) or Suspicious Activity Report (SAR), as it dynamically correlates between the alerts in which suspicious transaction reports have been generated and those that have been tagged as false positives by the investigators.”

Balakrishna Peddiraju, Head – Global Delivery & Engineering (AMLOCK), 3i Infotech said, “With increasing money laundering activities and financial frauds witnessed across organizations, there is a pressing need to curb such activities. Our AMLOCK Analytics solution helps detect unknown suspicious behavioral AML patterns, thereby improving operational efficiency. This solution is seamlessly integrated with AMLOCK and embedded with various machine learning and deep learning algorithms. It facilitates intense surveillance and reduces investigation time. It enables better alert triage and smarter investigations by providing astute guidance based on historical patterns for factors such as risk assessment.”

AMLOCK is 3i Infotech’s flagship AML solution that is multi-segmented and used by banks, insurance companies, Non-Banking Financial Companies (NBFCs) and mobile wallet companies to proactively detect and manage potential money laundering activities. It is an established global AML solution with over 100 installations across 35 countries. The offerings of the AMLOCKSuite include AMLOCKFCDMS, Financial Crime Detection and Management Solution, which provides end-to-end financial crime management for large enterprises; AMLOCKLite, which caters to AML requirement of small and medium institutions; and AMLOCK Analytics that brings the latest dimension of Analytics & AI to uncover hidden AML patterns / risks.  AMLOCKwas 3i Infotech’s successful application to Chartis Research’s RiskTech100 2020 rankings. The listing positions 3i Infotech among the world’s 100 major players in risk and compliance technology.

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DISCOVERING and harnessing fire unlocked more nutrition from food, feeding the bigger brains and bodies that are the hallmarks of modern humans. Google’s chief executive, Sundar Pichai, thinks his company’s development of artificial intelligence trumps that. “AI is one of the most important things that humanity is working on,” he told an event in California earlier this year. “It’s more profound than, I don’t know, electricity or fire.”

Hyperbolic analogies aside, Google’s AI techniques are becoming more powerful and more important to its business. But its use of AI is also generating controversy, both among its employees and the wider AI community.

One recent clash has centred on Google’s work with America’s Department of Defence (DoD). Under a contract signed in 2017 with the DoD, Google offers AI services, namely computer vision, to analyse military images. This might well improve the accuracy of strikes by military drones. Over the past month or so thousands of Google employees, including Jeff Dean, the firm’s AI chief, have signed a petition protesting against the work; at least 12 have resigned. On June 1st the boss of its cloud business, Diane Greene, conceded to those employees that the firm would not renew the contract when it expires next year.

The tech giant also published a set of seven principles which it promises will guide its use of AI. These included statements that the technology should be “socially beneficial” and “built and tested for safety”. More interesting still was what Google said it would not do. It would “proceed only where we believe that the benefits substantially outweigh the risks,” it stated. It eschewed the future supply of AI services to power smart weapons or norm-violating surveillance techniques. It would, though, keep working with the armed forces in other capacities.

Google’s retreat comes partly because its AI talent hails overwhelmingly from the computer-science departments of American universities, notes Jeremy Howard, founder of Fast.ai, an AI research institute. Many bring liberal, anti-war views from academia with them, which can put them in direct opposition with the firm in some areas. Since AI talent is scarce, the firm has to pay heed to the principles of its boffins, at least to some extent.

Military work is not the only sticking-point for Google’s use of AI. On June 7th a batch of patent applications made by DeepMind, a London-based sister company, were made public. The reaction was swift. Many warned that the patents would have a chilling effect on other innovators in the field. The patents have not yet been granted—indeed, they may not be—but the request flies in the face of the AI community’s accepted norms of openness and tech-sharing, says Miles Brundage, who studies AI policy at the University of Oxford. The standard defence offered on behalf of Google is that it does not have a history of patent abuse, and that it files them defensively in order to protect itself from future patent trolls. DeepMind’s patent strategy is understood to be chiefly defensive in nature.

Whatever Google’s intent, there are signs that the homogeneity of the AI community may lessen in future. New paths are being created to join the AI elite, other than a PhD in computer science. Hopefuls can take vocational courses offered by firms such as Udacity, an online-education firm; the tech giants also offer residencies to teach AI techniques to workers from different backgrounds. That might just lead to a less liberal, less vocal AI community. If so, such courses might serve corporate interests in more ways than one.

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