IBM and XPRIZE, a non-profit organisation focused on designing and implementing innovative competition models to solve real world problems, launched IBM Watson AI XPRIZE in 2016. The competition aims to accelerate the adoption of AI technologies to solve societal challenges.  

“The IBM Watson AI XPRIZE is more than a competition– it is a future-thinking initiative aimed at establishing a more balanced dialogue around AI– in particular about how humans and machines can collaborate towards a better future,” said Amir Banifatemi, General Manager of Innovation and Growth at XPRIZE. 

Earlier this week, the winners of the competition for 2021 were announced. Let’s take a look at the top three winners of the IBM Watson AI XPRIZE: 

Zzapp Malaria 

Zzapp Malaria won the grand prize, taking home $3 million and the People’s Choice Award for the ‘Most Inspiring Team’. 

Headquartered in Tel Aviv, Israel, Zzapp Malaria is on a mission to eliminate malaria, which kills over four lakh people every year. Co-founded by Arnon Houri-Yafin and Ari Eichler in 2016, Zzapp Malaria’s software solves operational challenges of targeting stagnant water bodies for mosquito control. 

Its AI algorithm analyses satellite images and topographical maps to identify malaria transmission hotspots, that is, areas where human populations and water bodies coincide. In addition, Zzapp Malaria uses a weather analysis algorithm to optimise the timing of interventions. The algorithm is exclusively developed by the IBM Data Science and AI Elite team for this purpose.

The AI strategies are then communicated to field workers using a map-based mobile application. The app further guides field workers to identify, report and treat water bodies. Finally, the data collected by field workers are fed back into the system to improve the algorithms and recommendations. 

At present, Zzap Malaria is running antimalarial in Ghana, Zanzibar and Tanzania. The startup has tied up with local partners, including municipalities, foundations, gold mines, and NGOs, to curb the mosquito menace. According to media reports, the co-founders soon plan to expand operations to Kenya, India and Mozambique. 

Before starting Zzapp Malaria, Co-founder and CEO Arnon was a key member of the team that developed Parasight, a machine vision-based malaria diagnostics device sold in more than 20 countries. 

Aifred Health 

Based out of Montreal, Aifred Health uses machine learning to treat depression. The company was founded in 2017 by Robert Fratila and Sonia Israel. 

Aifred Health’s clinical decision support tool is accessible on all devices, supporting telehealth and in-person appointments. The algorithm processes the patient’s behavioural health information. The output helps the physician understand where the patient stands on the disease cycle, monitor them over time and determine the way forward. Its solution uses AI to learn from a pool of patients to offer treatment and reduce the time taken for a patient to reach remission. Aifred Health uses IBM Watson Health to get access to millions of records of observational depression data and improve the model.

Once approved by FDA and Health Canada, Aifred Health plans to combine the clinical decision support tool with AI model for treatment selection, to create an end-to-end solution for the treatment of depression.

Aifred Health won the second prize of $1 million. 

Marinus Analytics 

Headquartered in Pittsburgh, United States, Marinus Analytics was founded in 2014 by Cara Jones and Emily Kennedy. The company uses AI and machine learning to deploy SaaS applications in the law enforcement and public sector. The company’s mission is to protect the vulnerable and end systemic exploitation.

Marinus Analytics’ services extract actionable insights from big data for detectives, social workers and cyber fraud investigators. Its flagship product Traffic Jam aims to prevent human trafficking, recover victims, and dismantle organised criminal networks. The firm uses tools including IBM Watson Discovery and IBM Watson Assistant. 

Marinus Analytics does not engage in the widespread use of facial recognition. It is one of the three organisations allowed to continue using Rekognition tools, to aid in the rescue of sex trafficking victims. 

The AI company is backed by the United States National Science Foundation and Bank of New York Mellon. Marinus Analytics won the third prize of $500k. 

The competition received 150 entries from across the globe. The teams were evaluated on the basis of four parameters– technical impact, real-world impact, scalability of real-world implications, ethics and safety. 

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IBM’s Uncertainty Qualification 360 is an open source library of Python algorithms for quantifying, estimating, and communicating the uncertainty of machine learning models.

IBM has created an open source Python library, called Uncertainty Qualification 360 or UQ360, that provides developers and data scientists with algorithms to quantify the uncertainty of machine learning predictions, with the goal of improving the transparency of machine learning models and trust in AI.

Available from IBM Research, UQ360 aims to address problems that result when AI systems based on deep learning make overconfident predictions. With the Python toolkit, users are provided algorithms to streamline the process of quantifying, evaluating, improving, and communicating the uncertainty of predictive models. Currently, the UQ360 toolkit provides 11 algorithms to estimate different types of uncertainties, collected behind a common interface. IBM also provides guidance on choosing UQ algorithms and metrics.

IBM stressed that overconfident predictions of AI systems can have serious consequences. Examples cited included a chatbot being unsure of when a pharmacy closes, resulting in a patient not getting needed medication, and the life-or-death importance of reliable uncertainy estimates in the detection of sepsis. UQ exposes the limits and potential failure points of predictive models, enabling AI to express that it is unsure and increasing the safety of deployment.

Previous IBM efforts to advance trust in AI have included the AI Fairness 360 toolkit, which mitigates bias in machine learning models; the Adversarial Robustness Toolbox, which is a Python library for machine learning security; and the AI Explainability 360 toolkit, which helps users comprehend how machine learning models predict labels.

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IBM Deploys AI to Induce Positive Disruptions

The colossal field of artificial intelligence has witnessed exponential growth in recent years. With the advent of deep neural networks (DNNs) that surpass human cognition and human intelligence, the post-modern world is now thriving on disruptive technologies and avant-garde innovations. Adhering to the ebb and flow of innovations and technological transformations, IBM is constantly experimenting with AI to explore the terra incognita and attach new dimensions to disruptive technology.

In recent days, IBM is striving to combine AI and hardware devices. Such a move will not only catalyze digital transformation in business industries and IT hubs but will also emerge as another disruptive technology at large.

The Exceptional Conflation of AI and Hardware: Aims and Objectives

Pushing the limits of AI, IBM is currently on a mission to instill AI in hardware. The IBM Research AI Hardware center is the hub of a team of academicians and industry leaders who are experimenting to give birth to the next wave of AI technologies. The mission of such a development is that it will deliver 2.5 times annual improvement in hardware that is capable of high compute efficiency. This development in hardware is deemed as one of the key components of IBM’s “Fluid Intelligence”.

With this development, IBM is looking forward to a 16-fold improvement in processing efficiency. IBM is delving into a world of analog computation, which can generate high performance in low power.

Additionally, IBM has also announced its third-generation digital AI core. IBM asserts that this will develop a new four-core design that accelerates the performance efficiency of training. This will significantly surpass and outpace the goal of 2.5X global improvements.

Another meticulous AI in hardware development is analog computing. IBM has devoted itself to experimenting with analog computers for decades. It has recently developed a chip using phase-memory (PCM) in order to encode the neural nets on a memory device.

It is said, “Time is money.” Business industries practice timeliness vehemently. This is taken as the primary reason to adopt high-end technologies to cope up with the cut-throat race in the market space.

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What more can artificial intelligence do?

IBM has designed an artificial intelligence system that can debate with humans. The company published a paper in the journal called Nature, where one of the team members described the AI system and how well it performed against a human opponent. Chris Reed, a professor in the University of Dundee has published a News & Views article in the same journal throwing light on the history and development of artificial intelligence as a disruptive technology based around the types of logic used in human arguments and the system created by IBM.

As Reed explains in his piece, debating is a skill that humans have been perfecting for thousands of years. It’s usually a type of discussion in which a person or a group persuades others that their opinion on a subject is right. As a step forward, the IBM team has designed an AI system to debate with humans in a live setting. The system is capable of listening to the moderators and opponents and responds in a feminine tone.

Traditionally in debates, people involved tend to cite people who can back up their claims. They might find these citations from prior research or quote well-known phrases used by notable people in the field of argument. Project Debater, as the IBM system is known, scans the internet for similar arguments and uses the information in ways that are convincing.

Another common debate practice is participants attempting to counter the arguments of the opponent. To handle that, Project Debater uses Watson, the IBM system that defeated contestants on the popular game show “Jeopardy”. This system listens to the arguments given by the opponents and searches on the web for rebuttals that were used by others about similar claims.

Project Debater was first tested back in 2019 in a debate session with Harish Natarajan, an expert debater. In that debate, IBM’s system did not defeat Natarajan, but the audience agreed that it performed outstandingly.

Experts at IBM believe that Project Debaters’ failure against Natarajan was an important milestone in the efforts to get robotic systems to master human language. IBM research director Dario Gil said that the experience “is not about winning or losing” but about creating an AI system “that can master the infinitely complex and rich world of human language.”

The Watson computer system used by Project Debater defeated a grand chess master, apart from winning the game show. Ranit Aharanov, manager of the Project Debater team talked in a discussion after the debate. There she said that the goal of the project is to help humans deal with complex decisions.

“It’s not a question of whether AI is going to be better than humans. It can debate both sides, so it can very quickly help you understand both sides of the problem, so you have a wider view of the problem and can make a better decision.”

Project Debater went through another test where it was asked to convince a panel of viewers that telemedicine was a good idea. Almost everyone in the panel agreed that the AI system managed to change their opinions on the topic of conversation which implied that AI systems can participate in human debates in the future, for example, in those that happen on social media.

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Artificial intelligence (AI) has been making great strides in recent years sometimes even coming close to being human-like. Now, in a new paper published in Nature magazine, IBM describes a system that can debate with humans and even sometimes win.

“Here we present Project Debater, an autonomous debating system that can engage in a competitive debate with humans,” write the authors. And the system is nothing short of extraordinary.

In tests of Project Debater, the AI was given only 15 minutes to research topics and prepare for debates. Each time, it proceeded to form an opening statement and even layer counterarguments. 

For the most part, the humans won the debate but in one instance it was able to change the stance of nine people. Not bad!

“Project Debater is a crucial step in the development of argument technology and in working with arguments as local phenomena. Its successes offer a tantalizing glimpse of how an AI system could work with the web of arguments that humans interpret with such apparent ease,” Chris Reed writes in a critique of the new project published in Nature magazine.

“Given the wildfires of fake news, the polarization of public opinion and the ubiquity of lazy reasoning, that ease belies an urgent need for humans to be supported in creating, processing, navigating and sharing complex arguments — support that AI might be able to supply.”

In other words, this new AI is not here to replace humans but rather to support them in building better arguments and reasoning with more nuance. If this subject interests you, Scientific American has done a great podcast episode with the research’s lead Noam Slonim which tackles amongst other things whether the AI actually understands the arguments it presents and what that means for the future of debating.

The post IBM Developed an AI System That Engages in Debates with Humans and Convinces Some appeared first on Artificial Intelligence.

Latest released the research study on Global Cloud Microservices Market, offers a detailed overview of the factors influencing the global business scope. Cloud Microservices Market research report shows the latest market insights, current situation analysis with upcoming trends and breakdown of the products and services. The report provides key statistics on the market status, size, share, growth factors of the Cloud Microservices. The study covers emerging player’s data, including: competitive landscape, sales, revenue and global market share of top manufacturers are AWS (United States),CA Technologies (United States),Contino (United Kingdom),CoScale (Belgium),IBM (United States),Idexcel (United States),Infosys (India),Kontena (Finland),Macaw Software (United States),Marlabs (United States),Microsoft (United States),Netifi (United States),NGINX (United States),OpenLegacy (United States),Oracle (United States),Pivotal Software (United States).

Definition:
A cloud microservices is known as a software development technique and a collection of services which are loosely coupled. Cloud Microservices is a method of developing software systems which tries to emphasis on developing single-function modules with the well-defined operations and interfaces. It is also known as the microservice architecture. Cloud microservices are gaining popularity among companies that need greater scalability and agility. In businesses across the industries, i.e., from telecommunications and retail to the financial services and manufacturing, IT teams are choosing cloud microservices, to develop new applications and break down monoliths

Keep yourself up-to-date with latest market trends and changing dynamics due to COVID Impact and Economic Slowdown globally. Maintain a competitive edge by sizing up with available business opportunity in Cloud Microservices Market various segments and emerging territory.

Market Trend:
Digital transformations

Market Drivers:
Proliferation of the microservices architecture
Customer-oriented business

Restraints:
Security and compliance

The Global Cloud Microservices Market segments and Market Data Break Down are illuminated below:
by Type (Stateless, Persistence, Aggregator), Industry (BFSI, IT and ITeS, Telecommunications, Government, Healthcare, Retail and eCommerce, Media and Entertainment, Transportation and Logistics, Manufacturing, Others (Education, Energy & Utilities, and Travel & Hospitality)), Services (Consulting services, Integration services, Training, support and maintained services), Organisation (SMEs, Large Enterprises), Deployment model (Public Cloud, Private Cloud, Hybrid Cloud), Component (Platform, Services)

Analyst at AMA have conducted special survey and have connected with opinion leaders and Industry experts from various region to minutely understand impact on growth as well as local reforms to fight the situation. A special chapter in the study presents Impact Analysis of COVID-19 on Global Cloud Microservices Market along with tables and graphs related to various country and segments showcasing impact on growth trends.

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa
Country Level Break-Up: United States, Canada, Mexico, Brazil, Argentina, Colombia, Chile, South Africa, Nigeria, Tunisia, Morocco, Germany, United Kingdom (UK), the Netherlands, Spain, Italy, Belgium, Austria, Turkey, Russia, France, Poland, Israel, United Arab Emirates, Qatar, Saudi Arabia, China, Japan, Taiwan, South Korea, Singapore, India, Australia and New Zealand etc.

Strategic Points Covered in Table of Content of Global Cloud Microservices Market:
Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Cloud Microservices market
Chapter 2: Exclusive Summary – the basic information of the Cloud Microservices Market.
Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of the Cloud Microservices
Chapter 4: Presenting the Cloud Microservices Market Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.
Chapter 5: Displaying market size by Type, End User and Region 2015-2020
Chapter 6: Evaluating the leading manufacturers of the Cloud Microservices market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
Chapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries (2021-2026).
Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

Finally, Cloud Microservices Market is a valuable source of guidance for individuals and companies in decision framework.

Data Sources & Methodology

The primary sources involves the industry experts from the Global Cloud Microservices Market including the management organizations, processing organizations, analytics service providers of the industry’s value chain. All primary sources were interviewed to gather and authenticate qualitative & quantitative information and determine the future prospects.

In the extensive primary research process undertaken for this study, the primary sources – Postal Surveys, telephone, Online & Face-to-Face Survey were considered to obtain and verify both qualitative and quantitative aspects of this research study. When it comes to secondary sources Company’s Annual reports, press Releases, Websites, Investor Presentation, Conference Call transcripts, Webinar, Journals, Regulators, National Customs and Industry Associations were given primary weight-age.

What benefits does AMA research study is going to provide?
• Latest industry influencing trends and development scenario
• Open up New Markets
• To Seize powerful market opportunities
• Key decision in planning and to further expand market share
• Identify Key Business Segments, Market proposition & Gap Analysis
• Assisting in allocating marketing investments

Definitively, this report will give you an unmistakable perspective on every single reality of the market without a need to allude to some other research report or an information source. Our report will give all of you the realities about the past, present, and eventual fate of the concerned Market.

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Southeast Asia.

Craig Francis (PR & Marketing Manager)
AMA Research & Media LLP
Unit No. 429, Parsonage Road Edison, NJ
New Jersey USA – 08837
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sales@advancemarketanalytics.com

Advance Market Analytics is Global leaders of Market Research Industry provides the quantified B2B research to Fortune 500 companies on high growth emerging opportunities which will impact more than 80% of worldwide companies’ revenues.
Our Analyst is tracking high growth study with detailed statistical and in-depth analysis of market trends & dynamics that provide a complete overview of the industry. We follow an extensive research methodology coupled with critical insights related industry factors and market forces to generate the best value for our clients. We Provides reliable primary and secondary data sources, our analysts and consultants derive informative and usable data suited for our clients business needs. The research study enables clients to meet varied market objectives a from global footprint expansion to supply chain optimization and from competitor profiling to M&As.

The post Cloud Microservices Market Will Hit Big Revenues In Future | IBM, Contino, AWS appeared first on Artificial Intelligence.

The University of South Carolina’s Office of Innovation, Partnerships, and Economic Engagement (OIPEE), in partnership with IBM and the USC/Columbia Technology Incubator, is offering the latest iteration of the IBM Skills Academy with a course in Data Science.

Participants who complete the course will earn a digital badge that reflects an ability to use advanced data science methods and tools to implement unique data models that can solve challenging problems across all industries. The course is designed so that participants can benefit and apply lessons to whatever career field they choose to pursue.

Last summer, UofSC hosted its first IBM Skills Academy course in Artificial Intelligence and graduated its first group of students. This new practitioner-level course in Data Science builds off that success and will teach participants how to extract knowledge from massive amounts of data, using methods such as statistics, machine learning, data mining, and predictive analytics. No matter the field of study or career path, a working knowledge of data science can help students, workers and organizations solve problems and gain a competitive advantage.

“Our mission is to help close the gap between academia and industry by leveraging our partnerships to give our students and graduates the necessary skills to be highly productive employees on day one,” said OIPEE Executive Director Bill Kirkland. “This goes for the community as well as the state’s flagship university, UofSC aspires to take the feedback we receive from industry and help build up the state’s workforce that can fill the demand for high-paying jobs, both now and in the future.”

OIPEE will host informational webinars. Register online for the Wednesday (Feb. 3) webinar or for the Feb. 9 webinar. Both informational sessions begin at 5:30 p.m.  The course begins Feb. 16, also at 5:30pm. The Data Science course will be taught by faculty from the College of Engineering and Computing and will explore concepts, relevant technologies and industry use cases. Classes will run twice a week and will include a mix of lectures, videos of subject matter experts, and interactive labs.

About IBM Skills Academy at UofSC

IBM Skills Academy is a comprehensive training and badging program designed to empower people of various backgrounds with the skills needed to excel in the job market.  Through a blended learning approach, participants learn at their own pace that best suits their individual learning style.  Once students pass the final exam, they will earn an IBM Digital Badge — a digital recognition of skills which can be shared on social and professional networks.  The badges — verified and administered by Pearson VUE Acclaim — can also be part of users’ digital signature and feature on networking platforms like LinkedIn. Future courses will be offered in Cybersecurity, Blockchain, Artificial Intelligence, Internet of Things (IoT), Cloud Computing, and Design Thinking.

The post UofSC launches new IBM Skills Academy training course in Data Science appeared first on Artificial Intelligence.

IBM Corp. said today it’s hoping to provide a standardized solution for developers to create and deploy machine learning models in production and make them portable to any cloud platform.

To do so, it said it’s open-sourcing the Kubeflow machine learning platform on Tekton, a continuous integration/continuous development platform developed by Google LLC. It’s popular with developers who use Kubernetes to manage containerized applications, which can run unchanged across many computing environments.

IBM said it created Kubeflow Pipelines on Tekton in response to the need for a more reliable solution for deploying, monitoring and governing machine learning models in production on any cloud platform. That’s important, IBM says, because hybrid cloud models are rapidly becoming the norm for many enterprises that want to take advantage of the benefits of running their most critical business applications across distributed computing environments.

One of the main complaints of data science and application development and delivery teams is the difficulty of creating portable machine learning models that can run on any cloud, Animesh Singh, IBM’s chief architect of data and AI open source, and Trent Gray-Donald, a distinguished engineer in AI, said in a blog post. They cite problems such as manual handoffs, frantic monitoring and loose governance, which can cause big delays in the deployment of new models.

IBM’s response to this has been to double down on Machine Learning Operations, or MLOps, which refers to a practice for collaboration and communication between data scientists and operations professionals to help manage production ML lifecycles.

IBM has since adopted Kubeflow Pipelines as the basis of its MLOps. Kubeflow is a machine learning platform that’s focused on distributed training, hyperparameter optimization, production model serving and management. Singh and Gray-Donald said Kubeflow was chosen primarily for its ability to tap into the Kubernetes ecosystem, which makes it compatible with containers and gives it greater scalability.

Kubeflow Pipelines also uses the Python interconnected that’s familiar to many developers for defining and deploying Pipelines. That enables metadata collection and lineage tracking of ML models.

Now, IBM has decided to open-source a revamped version of Kubeflow Pipelines especially for Tekton, which is an open-source project that provides a framework to create cloud-native continuous integration/continuous deployment pipelines quickly. With Tekton, developers can build, test, and deploy CI/CD pipelines across multiple cloud providers or on-premises systems by abstracting away the underlying implementation details.

“The decision to adopt Kubeflow Pipelines on our side came with an internal requirement to redesign Kubeflow Pipelines to run on top of Tekton,” Singh and Gray-Donald said.

Available today, the new Kubeflow Pipelines on Tekton provides Kubernetes-style resources for developers to declare CI/CD pipelines. It also introduces several new Custom Resource Definitions such as Task, Pipeline, TaskRun and PipelineRun.

In addition, IBM has created OpenShift Pipelines based on Tekton for its Red Hat OpenShift platform. That’s used to automate the building, testing and deployment of containerized applications for on-premises and public cloud platforms.

“Within IBM, we have standardized on Tekton as a cloud CI/CD engine, and OpenShift Pipelines is based on Tekton,” Singh and Gray-Donald said.

IBM is also planning to add to its stack with the coming launch of Watson AI Platform Pipelines. That will provide features that make it even easier for developers to build ML pipelines with a drag-and-drop canvas, pipeline components registry and production-grade logging. It will also provide better governance capabilities via integrations with IBM Watson Studio, Watson AutoAI, Watson Knowledge Catalog and others.

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With big data streaming into organizations worldwide at the rate of 2.5 quintillion bytes of data each day, it’s incumbent on organizations to determine just how much of this big data is vital and needed, and which portions of big data are excess and can be eliminated before the data ever enters corporate systems. If companies fail to do this, bandwidth, storage, and processing capabilities can be overrun–along with budgets.

For every operation and analysis companies perform with big data, the key is to define each business use case upfront and predetermine how much data you’ll really need to address the business case. Inevitably there will be some data that you just don’t need. Paring this data out of your data ingestion process is what I call narrowing the aperture of the lens through which data streams into your data repository.

Here are two divergent examples of data lens adjustment:

IBM RoboRXN and the mechanics of molecular formulation

When IBM designed its RoboRXN project, which takes in enormous quantities of unedited data from the worldwide open source community and others on potential molecular combinations for product formulation, decisions had to be made on how much of this data was relevant to the project they were working on.

The RoboRXN project focused on designing new molecules for pharmaceutical solutions, such as the COVID-19 vaccine. This meant that white papers, statistical research findings, and other sources of research that weren’t directly germane to the molecular formulation project that was being worked were not needed. What IBM decided to do was to implement artificial intelligence (AI) at the front of the data ingestion process while this enormous trove of unedited data was streaming in.

The AI algorithm posed one major question: Did each element of incoming data contain anything relevant to the focus of the project? For research that was not at all related to the project, or that was only distantly and tangentially related, the AI eliminated the data so it was never admitted to the data repository. In other words, the aperture of the data lens to the project’s data repository was tightened, admitting only those elements of data that were relevant to the project. As a result, data storage and processing were reduced, and so was cost.

SETI and the search for extraterrestrial life

Founded in 1984, the mission of the SETI Institute was to seek out extraterrestrial life. This was done by monitoring radio signals and emissions from space to determine if there were any repetitive patterns that could signify a communication from another life form. Scientists and volunteers participated in the SETI initiative, painstakingly examining mountains of unedited radio signals that flowed in ceaselessly.

In this effort, few assumptions could be made upfront about good versus bad data, because no one was entirely sure about what they were looking for. Consequently, there were few ways to “narrow” the aperture on the data lens, which had to be kept wide open. This resulted in high levels of processing, storage, and manual work.

What the Institute was able to do was to narrow down data after it had been searched in total for potential signals that might indicate intelligent life forms. At this point, only the signals with life potential needed to be stored in much smaller databases.

Lessons from SETI and IBM RoboRXN

The examples of IBM RoboRXN and SETI’s search for extraterrestrial life are at opposite ends of the data lens spectrum. In IBM’s case, there was the ability to narrow down the data lens aperture at the front of the process. This was not the case with SETI.

What these use cases tell data scientists and IT is that there is the potential to tamp down big data ingestion at a stage of pre-processing if you have a tight enough use case that does not have the potential of requiring data that initially is regarded as extraneous. In other cases, you have limited ability to tighten up data ingestion.

The goal in every big data project should be to include a task line that addresses how wide you need to set the aperture of the data lens for incoming data. This aperture can be adjusted upward or downward based upon the needs of each use case.

When you do this, you have a realistic way of controlling the processing, storage, and funding that will be needed for each project.

The post Big data: How wide should your lens be? It depends on your use appeared first on Artificial Intelligence.

New Jersey, United States,- The Internet of Things (IoT) Software Market has grown rapidly and contributes significantly to the global economy in terms of sales, growth rate, market share, and size. The Internet of Things (IoT) Software Market Report is a comprehensive research document that provides readers with valuable information to help understand the fundamentals of the Internet of Things (IoT) Software report. The report provides details on business strategies, market requirements, players dominating the market, and a futuristic perspective of the market.

The report will be updated with the latest economic scenario and market size in relation to the ongoing COVID-19 pandemic. The report covers the growth prospects as well as current and futuristic sales estimates in a post-COVID scenario. The report also covers changing market trends and dynamics due to the pandemic and provides an in-depth analysis of the impact of the crisis on the overall market.

The report covers extensive analysis of the key market players in the market, along with their business overview, expansion plans, and strategies. The key players studied in the report include:

Cisco Systems
Inc.
Bosch Software Innovations Gmbh
General Electric
Google Inc.
Amazon Web Services
Inc.
Hewlett Packard Enterprise
Intel Corporation
International Business Machine (ibm) Corporation
Microsoft Corporation
Oracle Corporation
Sap Se
The report provides a comprehensive analysis in an organized manner in the form of tables, graphs, charts, figures, and diagrams. The organized data paves the way for thorough examination and research of the current and future outlook of the market.

The examination of the Internet of Things (IoT) Software industry provides an in-depth analysis of the key market drivers, opportunities, challenges, and their impact on the working of the market. The technological advancements and product developments, driving the demands of the market are also covered in the report.

The report provides comprehensive data on the Internet of Things (IoT) Software market and its trends to assist the reader in formulating decisions to accelerate the business. The report provides a complete overview of the economic scenario of the market, along with benefits and limitations.

Internet of Things (IoT) Software market report contains industrial chain analysis and value chain analysis to provide a comprehensive view of the Internet of Things (IoT) Software market. The study is composed of market analysis along with a detailed analysis of the application segments, product types, market size, growth rate, and current and emerging trends in the industry.

The report further studies the segmentation of the market based on product types offered in the market and their end-use/applications.

In market segmentation by types of Internet of Things (IoT) Software, the report covers-

Real-time Streaming Analytics Software
Security Solution Software
Data Management Software
Remote Monitoring System Software
Network Bandwidth Management Software
In market segmentation by applications of the Internet of Things (IoT) Software, the report covers the following uses-

Building And Home Automation
Manufacturing
Retail
Transportation
Others

eographically, the market is spread across several key geographical regions, and the report covers the regional analysis as well as the production, consumption, revenue, and market share in those regions for the forecast period of 2020-2027. The regions include North America, Latin America, Europe, Asia-Pacific, and the Middle East and Africa.

Radical Coverage of the Internet of Things (IoT) Software Market:

Insightful information regarding the Internet of Things (IoT) Software market
Identification of growth in various segments and sub-segments of the Internet of Things (IoT) Software market
Strategic recommendations for investment opportunities
The report covers significant statistics related to the industry along with products, applications, price analysis, demand & supply, and production and consumptions rate
Emerging trends and current market segment analysis to help investors formulate new business strategies
Accelerates the decision-making process through the availability of the drivers and limitations

Key Questions Addressed in the Report:

Which segments are expected to show significant growth over the forecast period?
What is the forecast estimation of Internet of Things (IoT) Software market growth?
What are the factors that are likely to restrain the growth of the market?
What are the key driving factors of industry growth?
Which region is expected to dominate in the forecast period?
Which markets are significantly positive for developing businesses?
What is the expected growth rate of the industry throughout the forecast period?
Which market segments are expected to boost the growth of the industry?
Who are the dominating players of the Internet of Things (IoT) Software industry?
What are the strategic business plans undertaken by the key players of the industry?

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.

The post Internet of Things (IoT) Software Market Size 2020 appeared first on Artificial Intelligence.