The chances of partiality, in the process of data analysis, are extreme and it can vary from how a question is hypothesized and explored to how the data is sampled and organized. Bias can be introduced at any stage from defining and capturing the data set to run the analytics or AI or ML system. Hariharan Kolam, CEO, and founder of Findem, a people intelligence company stated in an interview, “Avoiding bias starts by recognizing that data bias exists, both in the data itself and in the people analyzing or using it,” Actually it is kind of impossible to be completely unbiased and biasedness is an existing element of human nature.

The Human Catalyst

Bias in data analysis can come from human sources because they use unrepresentative data sets, leading questions in surveys, and biased reporting and measurements. Often bias goes unnoticed until some decision is made based on the data, such as building a predictive model that turns out to be wrong. Although data scientists can never completely eliminate bias in data analysis, they can take countermeasures to look for it and mitigate issues in practice.

The Social Catalyst

Bias is also a moving target as societal definitions of fairness evolve. Reuters has reported an instance when the International Baccalaureate program had to cancel its annual exams for high school students in May due to COVID-19. Instead of using exams to grade students, the IB program used an algorithm to assign grades that were substantially lower than many students and their teachers expected.

Biasedness from Existing Data

Amazon’s previous recruiting tools showed preference toward men, who were more representative of their existing staff. The algorithms didn’t explicitly know or look at the gender of applicants, but they ended up being biased by other things they looked at that were indirectly linked to gender, such as sports, social activities, and adjectives used to describe accomplishments. In essence, the AI was picking up on these subtle differences and trying to find recruits that matched what they internally identified as successful.

Under-representing populations

Another big source of bias in data analysis can occur when certain populations are under-represented in the data. This kind of bias has had a tragic impact in medicine by failing to highlight important differences in heart disease symptoms between men and women, said Carlos Melendez, COO, and co-founder of Wovenware, a Puerto Rico-based nearshore services provider. Bias shows up in the form of gender, racial or economic status differences. It appears when data that trains algorithms do not account for the many factors that go into decision-making.

Cognitive biases

Cognitive bias leads to statistical bias, such as sampling or selection bias. Often analysis is conducted on available data or found in data that is stitched together instead of carefully constructed data sets. Both the original collection of the data and an analyst’s choice of what data to include or exclude creates sample bias. Selection bias occurs when the sample data that is gathered isn’t representative of the true future population of cases that the model will see. In times like this, it’s useful to move from static facts to event-based data sources that allow data to update over time to more accurately reflect the world we live in. This can include moving to dynamic dashboards and machine learning models that can be monitored and measured over time.

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Automated predictive analysis is making way for the greatest transformations in the industries!

Automated predictive analysis, or predictive analytics, uses historical data to predict future events. Throughout history, humans are obsessed with predicting the future. The fear of the unknown has led several scientific researchers and professors to develop technologies that can determine the future so that necessary steps can be taken to avoid drastic losses.

Predictive analytics has received a lot of attention in recent years due to its advances in supporting various technologies, particularly in the area of big data and artificial intelligence.

With the increased competition, businesses seek power over their competitors in bringing products and services to crowded markets. Data-driven predictive models can bring companies solutions to long-standing problems in terms of business operations. This technology provides a trove of information from which analytics tools and applications draw insights and predict the upcoming opportunities, suitable investments, and dangers in the market.

Businesses use tools like Hadoop and Spark to extract information from big data. These data sources might consist of transactional databases, equipment log files, images, videos, audios, sensors, and other types of data.

With all this data, tools are necessary to extract insights and trends. Predictive analytics finds patterns in data to build models that predict future outcomes. Other varieties of machine learning techniques are also available, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and other algorithms.

For decades, automated predictive analysis has been used by meteorologists to predict weather and climate forecasts. With time, this concept has been used to study consumer behavior, forecast supply and demand in economic statistics, and related purposes.

• How Can Artificial Intelligence Drive Predictive Analytics To New Heights
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• What Is Predictive Analytics And Can It Help You Achieve Business Objectives

Automated predictive technology: Today and beyond 

Data is the core of predictive analytics. Earlier, when there were no computers, businesses used other creative ways to understand what the customers want and predict market conditions. These ways did not involve technological tools or applications.

Currently, one of the most vital industrial applications of predictive models includes energy load forecasting to predict energy demand in the future. Energy producers, grid operators, and traders need accurate predictions of energy load to make decisions for managing tasks in electric grids. Grid operators use data to draw actionable insights.

Artificial intelligence and predictive technology, have revolutionized the way advertisers and marketers work. Targeted advertising uses data like previously purchased products, location, and age to serve the target audience. Today, consumer profiles are much more advanced, and enterprises can gather information from various sources.

Predictive analytics is also used to measure vehicle and pedestrian traffic to coordinate traffic lights, public transportation, and even pedestrian crosswalks to facilitate convenience and efficiency in community design. This also boosts the safety of the public and allocates emergency services more efficiently by predicting the number of officers needed on a task and reassigns posts accordingly.

Automated predictive technology, has played a crucial role in facilitating better medical resources. This technology helps improve the patients’ health outcomes. Rather than completely relying on the patient’s medical history, predictive systems can generate data from a broad spectrum of symptoms, data of other patients, and the treatments used to cure the disease.

AI and machine learning have provided us with various ways through which we can predict the future. With the growing technological evolution in automation and data analysis, our lives will be changed forever and for the better.

The post HOW HAS AUTOMATED PREDICTIVE ANALYSIS DEVELOPED OVER THE YEARS appeared first on Artificial Intelligence.

A study finds image analysis using machine learning can identify haematological malignancies.

Image analysis is typically used to extract meaningful information from images. It can perform tasks like finding shapes, identifying edges, removing noise, counting objects, etc. for image quality. Recently, a study demonstrated that image analysis utilizing neural networks can help detect details in tissue samples that are difficult to determine with the human eye. Myelodysplastic syndrome (MDS) is a disease of the stem cells in the bone marrow, which affects the maturation and differentiation of blood cells. Diagnosing MDS requires a bone marrow sample to investigate genetic changes in the bone marrow cells.

Annually, some 200 Finns are diagnosed with MDS, which can develop into acute leukaemia. The incidence of MDS globally is 4 cases per 100,000 person years. The syndrome is classified into groups to find out the nature of the disorder in more detail.

In the University of Helsinki study, microscopic images of patients’ bone marrow samples suffering from myelodysplastic syndrome were analysed utilising an image analysis technique based on machine learning. The samples were stained with haematoxylin and eosin (H&E staining), a procedure of routine diagnostics for the disease. The slides were digitised and analysed using computational deep learning models.

The study was published in the Blood Cancer Discovery, a journal of the American Association for Cancer Research. The results can be explored with an interactive tool: http://hruh-20.it.helsinki.fi/mds_visualization/.

With machine learning, the digital image dataset could be assessed to accurately identify the most common genetic mutations affecting the progression of the syndrome, such as acquired mutations and chromosomal aberrations. The higher the number of abnormal cells in the samples, the higher the reliability of the results generated by the prognostic models.

The study uses the data analysis technique to support the diagnosis. One of the greatest challenges of leveraging neural network models is to understand the criteria on which they base their conclusions drawn from data, such as information contained in images. The University of Helsinki study succeeded in determining what deep learning models see in tissue samples when they have been taught to look for, for example, genetic mutations related to MDS. The technique provides new information on the effects of complex diseases on bone marrow cells and the surrounding tissues.

According to Professor Satu Mustjoki, ‘the study confirms that computational analysis helps to identify features that elude the human eye. Moreover, data analysis helps to collect quantitative data on cellular changes and their relevance to the patient’s prognosis.’

Part of the analytics carried out in the study was implemented using the Helsinki University Hospital (HUS) data lake environment, which enables the efficient collection and analysis of extensive clinical datasets.

“We’ve developed solutions to structure and analyse data stored in the HUS data lake. Image analysis helps us analyse large quantities of biopsies and rapidly produce diverse information on disease progression. The techniques developed in the project are suited to other projects as well, and they are perfect examples of digitalizing medical science,” says doctoral student Oscar Bruck.

Ph.D. Olivier Elemento from the Caryl and Israel Englander Institute for Precision Medicine says, “[This] study provides new insights into the pathobiology of MDS and paves the way for increased use of artificial intelligence for the assessment and diagnosis of hematological malignancies.”

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The demand for these sought-after positions continues to grow in importance, according to analysis of job openings for March 2021 conducted by LHH.

Many tech workers find themselves on the precipice of change: their organizations may decide to continue remote work for the foreseeable future, they may adopt a hybrid work schedule or they’ll return to on-premises full time. But while a recent study showed that most employees said they’ll stay in their current positions, they also said they expected a greater return from their employers, which means if they don’t get that “return,” they’ll be on the job market. 

SEE: Hiring Kit: Technical Recruiter (TechRepublic Premium)

Many organizations were hit hard by the impact of the pandemic and, coupled with the safety protocols required for workers who do go into the office, they might not be able to accommodate employees’ demands. This represents potential for the tech industry, with pros looking for new positions and companies looking for new talent. LHH, formerly Lee Hecht Harrison, global provider of talent and leadership development, career transition and coaching, analyzed job openings for March 2021, and its findings can provide insight for tech pros, whether they want to remain in their jobs or if they might consider “putting feelers out.” 

While the LHH Job Bulletin (where the insights can be found) covers many industries, it does devote a portion to tech news. Here’s a look at what is covered in the report.

This week’s 10 most wanted skills based on job postings 

1. Communication, 646,575  
2. Dedication, 455,651
3. Analysis, 409,243
4. Leadership, 404,484
5. Collaboration, 386,936
6. Scheduling, 358,769
7. Operations, 356,270
8. Innovation, 350,042
9. Written communications, 293,720  
10. Verbal communications, 288,513

Top tech jobs in top 15 job categories

LHH noted that LinkedIn recently highlighted the top 15 job categories based on demand and growth and offered clients a look at where some of the tech-related jobs landed on that list.

No. 6 Digital marketing: COVID-19, while 99.9% awful, caused a big spike in online shopping, which in turn created a demand for marketers well versed in reaching people online. Digital marketing hires grew nearly 33% in 2020 from 2019. Job titles in this category include digital marketing specialist, social media manager, marketing representative and search engine optimization specialist. The salary range is $48,000 to $96,000 per year.

No. 9 Digital content: The demand for online entertainment went stratospheric in 2020. People sheltering at home binge watched and were hungry for content. Digital content creators were in demand, too, and grew 49% in 2020. Job titles include content coordinator, writing consultant, podcaster and blogger. The salary range is $46,000 to $62,400 per year.

No. 11 Software: With many employees sent to work remotely, being online was everything in 2020. Specialists needed to keep the online world up and running grew nearly 25% in 2020. Job titles in this category include web developer, full-stack engineer, front-end developer, and game developer. The salary range is $77,500 to $104,000 per year.

No. 13 User experience: The swift move to online everything in 2020 meant there was a need for experts who specialize in the interactions between people and the digital world, growing 20% in 2020. Job titles in this category include user-experience designer, product design consultant, user interface designer, and user experience researcher. The salary range is $41,600 to $65,000 per year.

#14 Data science: With everyone reliant on data and its capabilities, experts were in high demand. Hiring for data science positions grew nearly 46% in 2020. Job titles in this category include data scientist, data science specialist, and data management analyst. The salary range is $100,000 to $130,000 per year.

#15 Artificial intelligence: The massive, pandemic-induced employment shifts, layoffs, and business disruptions in 2020 resulted in companies looking to artificial intelligence as a way to keep up with increased demand, while safeguarding their businesses from future disruptions. AI hiring grew 32% in 2020. Some of the job titles in this category include machine learning engineer, artificial intelligence specialist, and machine learning researcher. The salary range is $124,000 to $150,000 per year.

Top 5 tech jobs with fastest-growing salaries 

Apart from the top tier of healthcare, the tech industry offers some of the highest salaries among industries. LHH analyzed popular roles and revealed the percentage of growth between 2019 and 2020.

• Cybersecurity analyst–16.3% growth
• Data scientist–12.8% growth
• DevOps engineer–12.2% growth
• Technical support engineer–8.2% growth 
• Cloud architect/engineer–6.3% growth

Companies with tech job openings, according to LHH

• Novacoast,a cybersecurity company, will expand in Wichita, Kansas. The company will open a Security Operations Center initially hiring 60 employees with plans to expand over the next few years.
• Pendo, a startup in North Carolina, prevailed during a pandemic-stricken year. The company plans to hire 400 more employees this year to fuel that growth as it invests heavily in its presence overseas and looks to nab more large customers to its platform. There are currently 169 open roles.
• Infosys will hire 300 workers in Pennsylvania as part of its local hiring strategy in the U.S. Infosys will recruit for a range of opportunities across technology and digital services, client administration and operations.
• Zones, an IT company,is hiring for advanced technology executives across the U.S.
• HCL Technologies specializes in IT services and consulting. It delivers innovative technology solutions built around digital, Internet of Things, cloud, automation, cybersecurity, analytics, infrastructure management and engineering.
• TEKsystems has opportunities for technical PM, product manager, SW developer, QA, mobile developer, Python developer, DevOps engineer, site reliability engineer, video streaming engineer, cloud engineer, security engineer, data center technician, and more.

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The global Storage in Big Data Market was accounted for US$ 17,391.4 Mn in terms of value in 2019 and is expected to grow at CAGR of 20.4% for the period 2020-2027.

Big data storage refers to a compute and storage architecture that gathers and operates vast data sets and allows real-time data analytics. Many companies employ big data analytics to collect greater intelligence from metadata. Big data storage allows the storage and sorting of big data in such a way that it can be easily used, accessed, and processed by applications and services working on big data. Moreover, big data can be flexibly scaled as required. Many end-use industries employ big data storage including BFIS, media and entertainment, IT and telecommunications, healthcare and medical, transportation, logistics, retail, etc.

In a software-based storage solution, the storage controller software is disassociated from hardware and takes advantage of industry-standard hardware platforms, in order to deliver a complete range of storage services. This allows different solutions for data storage, data access interfaces, services, and can be delivered in various forms including on cloud. According to Intel Corporation’s study 2016, enterprises are shifting towards software-based storage as performance, capital expenses and scaling are the top three factors considered by data center managers. However, there are several approaches that can be used while deploying software-based storage such as Do-It-Yourself solutions, turnkey solutions and converged and hyper-converged solutions. Hence, these factors are expected to support growth of the global storage in the big data market in the near future.

Which are Compay Profile Plays Major Role in Storage in Big Data (AML) Market?
Key players operating in the global storage in big data market are MemSQL Inc., Google Inc., Hitachi Data Systems Corporation, Microsoft Corporation, Hewlett Packard Enterprise, Amazon Web Services, Inc., Teradata Corporation, VMware, Inc., SAP SE, IBM Corporation, Oracle Corporation, Dell EMC, and SAS Institute Inc.

What are the Key Segments in the Market By Types?
Global Storage in Big Data Market, By Segment:
• Hardware
◦ DAS – internal (OEM)
◦ DAS – external (OEM)
◦ DAS – other (ODM Direct)
◦ ESCON/FICON
◦ NAS
◦ SAN
◦ Tape Systems and Media
• Software
• Services

What are the Key Segments in the Market By End-use Sector ?
Global Storage in Big Data Market, By Industry:
• BFSI
• IT and Telecommunications
• Transportation, Logistics & Retail
• Healthcare and Medical
• Media and Entertainment
• Others

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Analytics Insight recently published its first AI Review report to offer insights on how artificial intelligence service providers are positioned in the industry. Featuring four different categories, viz., Leaders, Disruptor, Pioneers, Challenger, the report highlights the top vendors in each category and points out their market scope and ability to disrupt the AI market.

The report has evaluated 15 artificial intelligence companies, taking into account their market influence and market potential, thus providing a 360-degree view of these vendors. The core factors in determining market influence include, area of focus, solutions, industries, market revenue and geographic presence. Whereas, the market potential is gauged using attributes like funding, innovation, and strategy. 

It is important to note that the vendor selection and assessment are based on key insights and attributes which were validated by analysts and inputs from global CXO advisors of Analytics Insight. Further, Analytics Insight does not endorse any vendor, product or service depicted in its AI Matrix report. 

Besides, this report aims to act as a reference for companies around the world when considering and selecting artificial intelligence service providers for various industrial functions. It can also help companies who want to analyze and compare their market competitors. This information can prove resourceful when attempting to improve one’s own company in areas where others are weak, giving them a market advantage.

Analytics Insight added in its report that, “Since the AI Matrix is a graphical presentation of a vendor’s strengths and capabilities, understanding this can enable business leaders to look for recommendations in the Artificial Intelligence niche, with respect to their business and technology needs.”

The contextualization by Analytics Insight is its attempt to give a quick snapshot of the notable players in the artificial intelligence industry. However, it advises companies not to select vendors only considering their ratings or other designation.

The Analytics Insight AI Matrix report has placed the top visionaries like Databricks, SenseTime, H2O.ai and DataRobot in the Leaders, while Dataiku, DeepMind, C3.ai and SentinelOne were positioned in the Pioneers’. On the other hand, the Disruptors  include Feedzai, Dataminr, AiBrain and Scale AI. Finally, vendors Nauto, Element AI and People.ai feature in Challengers. 

The post ANALYTICS INSIGHT PUBLISHES VENDOR ANALYSIS REPORT FOR ARTIFICIAL INTELLIGENCE appeared first on Artificial Intelligence.

Artificial intelligence (AI) has become a buzzword in technology in both civilian and military contexts. With interest comes a radical increase in extravagant promises, wild speculation, and over-the-top fantasies, coupled with funding to attempt to make them all possible. In spite of this fervor, AI technology must overcome several hurdles: it is costly, susceptible to data poisoning and bad design, difficult for humans to understand, and tailored for specific problems. No amount of money has eradicated these challenges, yet companies and governments have plunged headlong into developing and adopting AI wherever possible. This has bred a desire to determine who is “ahead” in the AI “race,” often by examining who is deploying or planning to deploy an AI system. But given the many problems AI faces as a technology its deployment is less of a clue about its quality and more of a snapshot of the culture and worldview of the deployer. Instead, measuring the AI race is best done by not looking at AI deployment but by taking a broader view of the underlying scientific capacity to produce it in the future.

AI Basics: The Minds We Create

AI is both a futuristic fantasy as well as an omnipresent aspect of modern life. Artificial intelligence is a wide term that broadly encompasses anything that simulates human intelligence. It ranges from the narrow AI already present in our day-to-day lives that focuses on one specific problem (chess playing programs, email spam filters, and Roombas) to the general artificial intelligence that is the subject of science fiction (Rachel from Blade Runner, R2-D2 in Star Wars, and HAL 9000 in 2001: A Space Odyssey). Even the narrow form that we currently have and continually improve, can have significant consequences for the world by compressing time scales for decisions, automating repetitive menial tasks, sorting through large masses of data, and optimizing human behavior. The dream of general artificial intelligence has been long deferred and is likely to remain elusive if not impossible, and most progress remains with narrow AI. As early as the 1950’s researchers were conceptualizing thinking machines and developed rudimentary versions of them that evolved into “simple” everyday programs, like computer opponents in video games.

Machine learning followed quickly, but underwent a renaissance in the early 21st century when it became the most common method of developing AI programs, to the extent that it has now become nearly synonymous with AI. Machine learning creates algorithms that allow computers to improve by consuming large amounts of data and using past “experience” to guide current and future actions. This can be done through supervised learning, where humans provide correct answers to teach the computer; unsupervised learning, where the machine is given unlabeled data to find its own patterns; and reinforced learning, where the program uses trial and error to solve problems and is rewarded or penalized based on its decision. Machine learning has produced many of the startling advances in AI over the last decade such as drastic improvements to facial recognition and self-driving cars, and has given birth to a method that seeks to use the lessons of biology to create systems that process data similar to brains: deep learning. This is characterized by artificial neural networks where data is broken down to be examined by “neurons” that individually handle a specific question (e.g. whether an object in a picture is red) and describes how confident it is in its assessment, and the network compiles these answers for a final assessment.

But despite the advances that AI has undergone since the machine learning renaissance and its nearly limitless theoretical applications, it remains opaque, fragile, and difficult to develop.

Challenges: The Human Element

The way that AI systems are developed naturally creates doubts about their ability to function in untested environments, namely the requirement of large amounts of data inputs, the necessity that they be nearly perfect, and the effects of the preconceived notions of its creators. First, lack of, or erroneous, data is one of the largest challenges, especially when relying on machine learning techniques. To teach a computer to recognize a bird, it must be fed thousands of pictures to “learn” a bird’s distinguishing features, which naturally limits use in fields with few examples. Additionally, if even a tiny portion of the data is incorrect (as little as 3%), the system may develop incorrect assumptions or suffer drastic decreases in performance. Finally, the system may also recreate assumptions and prejudices—racist, sexist, elitist, or otherwise—from extant data that already contains inherent biases, such as resume archives or police records. These could also be coded in as programmers inadvertently impart their own cognitive biases into the machine learning algorithms they design.

This propensity for deep-seated decision-making problems, which may only become evident well after development, will prove problematic to those that want to rely heavily on AI, especially concerning issues of national security. Because of the inherent danger of ceding critical functions to untested machines, plans to deploy AI programs should not be seen primarily as a reflection of their own quality, but of an organization’s culture, risk tolerance, and goals.

The acceptability of some degree of uncertainty also exacerbates the difficulties in integrating AI with human overseers. One option is a human-in-the-loop system where human overseers are integrated throughout the decision process. Another is human-on-the-loop system where the AI remains nearly autonomous with only minor human oversight. In other words, organizations must decide whether to give humans the ability to override a machine’s possibly better decision that they cannot understand. The alternative is to cede human oversight that may prevent disasters that might be obvious to organic minds. Naturally, the choice will depend on the stakes: militaries may be much more likely to allow a machine to control leave schedules without human guidance rather than anti-missile defenses.

Again, as with doubt about decision integrity, the manner in which an organization integrates AI into the decision-making process can tell us a great deal. Having a human-in-the-loop system signals that an organization would like to improve the efficiency of a system considered mostly acceptable as is. A human-on-the-loop system signals greater risk tolerance, but also betrays a desire to exert more effort to catch up to, or surpass, the state of the art in the field.

The Global AI Race: Measuring the Unmeasurable

Research and development funding is a key component of scientific advances in the modern world, and is often relied on as a metric to chart progress in AI. The connection is often specious, however; the scientific process is often filled with dead ends, ruined hypotheses, and specific research questions with no broader significance. This last point is particularly salient to artificial intelligence because of the tailored nature of specific AI applications, which requires a different design for each problem it tackles. AI that directs traffic, for example, is completely worthless at driving cars. For especially challenging questions (e.g. planning nuclear strategy), development is an open-ended financial commitment with no promise of results.

It becomes difficult, therefore, to accurately assess achievement by simply using the amount spent on a project as a proxy for progress. Perhaps money is being spent on dead ends, an incorrect hypothesis, or even to fool others into thinking that progress is being made. Instead, we should see money as a reflection of what the spender values. Project spending then is not an effective metric of the progress of AI development, but of how important a research question is to the one asking it.

But that importance provides a value for analysis, regardless of its inapplicability to measuring the AI race: the decision-making process can speak volumes about the deployer’s priorities, culture, risk tolerance, and vision. Ironically, the manner in which AI is deployed says far more about the political, economic, and social nature of the group deploying it than it does about technological capability or maturity. In that way, deployment plans offer useful information for others. This is particularly valid in examinations of government plans. Examination of plans have produced insight such as using Chinese AI documents to deduce where they see weakness in their own IT economy, finding that banks overstate the use of chatbots to appear convenient for their customers, or noting that European documents attempt to create a distinctive European approach to the development of AI in both style and substance. It is here that examinations of AI deployment plans offer their real value.

There are instead much better ways to measure progress in AI. While technology rapidly changes, traditional metrics of scientific capacity provide a more nuanced base to measure AI from and are harder to manipulate, which makes them more effective than measuring the outputs of AI projects. The most relevant include: scientists as a proportion of population, papers produced and number of citations, research and development spending generally (as opposed to the focus on specific projects), and number of universities and STEM students. Measuring any scientific process is naturally fraught with peril due to the potential for dead-end research, but taken broadly these metrics give a far better picture of the ability of a state or organization to innovate in AI technology. Multiple metrics should always be used however; any focus on a specific metric (e.g. research spending) will make it just as easy to game the system as relying on AI deployment does. Such a narrow focus also distorts the view of the AI landscape. Consider, for example, the intense insecurity over the position of the United States despite its continuing leadership in terms of talent, number of papers cited, and quality of universities.

Recharging the Scientific Base

The U.S. National Security Commission on AI draft report notes, “The nation with the most resilient and productive economic base will be best positioned to seize the mantle of world leadership.” This statement encapsulates the nature of the AI race, and naturally, measuring it. If a government or a company wishes to take a leadership position in the race, the goal should be to stimulate the base that will produce it, not actively promote a specific project, division, or objective. This involves tried and true (but oft neglected) policies like promoting STEM education, training new researchers internally, attracting foreign talent with incentives, providing funding for research and development (especially if it forms a baseline for future work such as computer security or resilience), and ensuring that researchers have access to the IT hardware that they need through adequate manufacturing and procurement processes.

These suggestions are often neglected in the United States in particular because of intense politicization of domestic priorities such as education policy (affecting universities), immigration policy (affecting the attraction of foreign talent), and economic policy (affecting manufacturing and procurement). At the same time, it is not only about providing more funding but streamlining processes that enable scientific capacity. For example, the system for receiving scientific research grants is byzantine, time-consuming, and stifling with different government agencies having overlapping funding responsibilities. Efforts should be made to ensure that applying for grants is not only easier, but that it promotes broader scientific inquiries. By solving problems like these, leaders invest in the components that will create the winning position in the AI race, and observers can determine who is making the strides to lead now, as well as in the future.

In the information age, the deployment of new technologies and their level of advancement have become key metrics in measuring power and effectiveness, but these are often flawed. Particularly for AI projects, research budgets, task assignments, and roles relative to humans demonstrate little about the state of the technology itself. Given the many fundamental problems with deploying AI, risk tolerance and strategic culture play much more of a role in determining how it is carried out: the more risk tolerant an organization is and the more it feels challenged by competitors, the more likely it will adopt AI for critical functions. Rather than examining AI deployment plans to see which country or organization is “ahead,” we should use them to study their worldview and strategic outlook. Instead, we should rely on overall scientific capacity to determine pole positions in the AI race.

The post Rethinking The Artificial Intelligence Race – Analysis appeared first on Artificial Intelligence.

“A SWOT Analysis of Data Science Platform, Professional Survey Report Including Top Most Global Players Analysis with CAGR and Stock Market Up and Down.”

The global “Data Science Platform market” research report is crafted with the concise assessment and extensive understanding of the realistic data of the global Data Science Platform market. Data collected cover various industry trends and demands linked with the manufacturing goods & services. The meticulous data gathered makes the strategic planning procedure simple. It also helps in creating leading tread alternatives. In addition, it also highlights the dominating players in the market joined with their market share. The well-established players in the market are Dataiku, IBM, Datarobot, Wolfram, Feature Labs, Google, Cloudera, Rexer Analytics, Continuum Analytics, Domino Data Lab, Datarpm, Rapidminer, Bridgei2i Analytics, Alteryx,Microsoft.

Click here to access the report

Most of the data is presented in the form of graphical demonstration with accurately intended figures. The performance of the related key participants, suppliers, and vendors is furthermore explained in the global Data Science Platform report. It also underscores the restraints and drivers keenly from the prudent perceptive of our specialists. Additionally, the global Data Science Platform market report covers the major product categories and segments On-Premises, On-Demand along with their sub-segments Marketing, Sales, Logistics, Risk, Customer Support, Human Resources, Operations in detail.

The perfect demonstration of the most recent improvements and latest technologies offers the user with a free hand to grow ultramodern products and procedures to update the service offering. This ultimately helps to work with perfect business options and apply smart implementations. The global Data Science Platform report highlights the latest trends, growth, new opportunities, and dormant tricks to provide an inclusive view of the global Data Science Platform market. Demand proportion and development of innovative technologies are some of the key points explained in the global Data Science Platform market research report.

The research report also highlights the in-depth analysis of various decisive parameters such as profit & loss statistics, product value, production capability, and many more. The report showcases back-to-back parameters such as application, improvement, product growth, and varied structures & processes. It also highlights a variety of modifications done to improve the process functioning of the global Data Science Platform market.

A well-crafted Data Science Platform market research report is based on the primary and secondary source. It is presented in a more communicative and expressed format that allows the customer to set up a complete plan for the development and growth of their businesses for the anticipated period.

The additional geographical segments are also mentioned in the empirical report.

North America: U.S., Canada, Rest of North America
Europe: UK, Germany, France, Italy, Spain, Rest of Europe
Asia Pacific: China, Japan, India, Southeast Asia, North Korea, South Korea, Rest of Asia Pacific
Latin America: Brazil, Argentina, Rest of Latin America
Middle East and Africa: GCC Countries, South Africa, Rest of Middle East & Africa

Impact Of COVID-19

The most recent report includes extensive coverage of the significant impact of the COVID-19 pandemic on the Heated Jacket division. The coronavirus epidemic is having an enormous impact on the global economic landscape and thus on this special line of business. Therefore, the report offers the reader a clear concept of the current scenario of this line of business and estimates the aftermath of COVID-19.

There are 15 Chapters to display the Global Data Science Platform market

Chapter 1, Definition, Specifications and Classification of Data Science Platform, Applications of Data Science Platform, Market Segment by Regions;
Chapter 2, Manufacturing Cost Structure, Raw Material and Suppliers, Manufacturing Process, Industry Chain Structure;
Chapter 3, Technical Data and Manufacturing Plants Analysis of Data Science Platform, Capacity and Commercial Production Date, Manufacturing Plants Distribution, R&D Status and Technology Source, Raw Materials Sources Analysis;
Chapter 4, Overall Market Analysis, Capacity Analysis (Company Segment), Sales Analysis (Company Segment), Sales Price Analysis (Company Segment);
Chapter 5 and 6, Regional Market Analysis that includes United States, China, Europe, Japan, Korea & Taiwan, Data Science Platform Segment Market Analysis (by Type);
Chapter 7 and 8, The Data Science Platform Segment Market Analysis (by Application) Major Manufacturers Analysis of Data Science Platform ;
Chapter 9, Market Trend Analysis, Regional Market Trend, Market Trend by Product Type On-Premises, On-Demand, Market Trend by Application Marketing, Sales, Logistics, Risk, Customer Support, Human Resources, Operations;
Chapter 10, Regional Marketing Type Analysis, International Trade Type Analysis, Supply Chain Analysis;
Chapter 11, The Consumers Analysis of Global Data Science Platform ;
Chapter 12, Data Science Platform Research Findings and Conclusion, Appendix, methodology and data source;
Chapter 13, 14 and 15, Data Science Platform sales channel, distributors, traders, dealers, Research Findings and Conclusion, appendix and data source.

Reasons for Buying Data Science Platform market

• This report provides pin-point analysis for changing competitive dynamics
• It provides a forward looking perspective on different factors driving or restraining market growth
• It provides a six-year forecast assessed on the basis of how the market is predicted to grow
• It helps in understanding the key product segments and their future
• It provides pin point analysis of changing competition dynamics and keeps you ahead of competitors
• It helps in making informed business decisions by having complete insights of market and by making in-depth analysis of market segments

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The post Global Data Science Platform Market Size, Share & Trends Analysis Report 2021-2027 appeared first on Artificial Intelligence.

Latest update on Data Science Platform Market Analysis report published with an extensive market research, Data Science Platform market growth analysis and Projection by – 2025. this report is highly predictive as it holds the over all market analysis of topmost companies into the Data Science Platform industry. With the classified Data Science Platform market research based on various growing regions this report provide leading players portfolio along with sales, growth, market share and so on.

Global Data Science Platform Market is valued approximately USD 237.82 Billion in 2019 and is anticipated to grow with a healthy growth rate of more than 30% over the forecast period 2019-2026. Data science is a start to consolidate insights, information search, and their related procedures to comprehend and assess genuine information from the raw data. It gives techniques and methods drawn from numerous zones inside the wide sections of arithmetic, databases, data science, measurements and software engineering particularly from the subdomains of machine learning, bunch examination, information mining, and representation. The global impact of COVID-19 results in slow down of numerous economies and business across the world which may hinder the growth of data science platform market

The report thoroughly covers the Data Science Platform market by type, applications and regions. The report provides an balanced and detailed analysis of the on-going Data Science Platform trends, opportunities/high growth areas, Data Science Platform market drivers which would help the investors to device and align their market strategies according to the current and future market dynamics.

The data science tools are largely accepted and demanded across the globe in various end-use industries due to the global rise in adoption of advanced technologies such as the internet of things (IoT), machine learning (ML), and AI. Such advanced technologies derives a lot of data from multiple devices such as cameras, smartphones, and routers, among others, which can be utilized to enhance business operations therefore big data analytics are used within an organization to increasingly focusing on optimizing structured and unstructured data to produce meaningful insights. Therefore, rapid growth in big data analytics across the globe in business operations is expected to propel the growth of market over the forecast years. For instance: As per the study by Wikibon, the worldwide big data market revenue for software and service is projected to grow from USD 42 billion in 2018 to USD 103 billion till 2027. In addition, growing demand for public cloud and adoption of artificial intelligence in developing economies is expected to propel the growth of market over the upcoming period

The report provides insights on the following sections:

• Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players in the Global Data Science Platform Market.
• Product Development and Innovation: Provides intelligent insights on future technologies, R & D activities, and new product developments in the Global Data Science Platform Market.
• Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets for the Global Data Science Platform Market.
• Market Diversification: Provides detailed information about new products launches, untapped geographies, recent developments, and investments in the Global Data Science Platform Market.
• Competitive Assessment and Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players in the Global Data Science Platform Market.

For instance: According to the study by International Financial Corporation: China GDP is expected to grow with USD 38 trillion by 2030, with the USD 7 trillion of that coming from AI through new business creation and upgradation of existing business in terms of improved efficiency and cost reduction. Also, according to the study by Accenture, AI has potential to add USD 957 billion of about 15% growth in India’s current gross value till 2035. However, Stringent government rules and regulations associated with Data science platform is hampering the growth of market over the forthcoming period.

Major Companies covered in  Data Science Platform market report are: Microsoft Corporation, IBM Corporation , SAS Institute, Inc., SAP SE, RapidMiner, Inc., Dataiku SAS, Alteryx, Inc, Fair Issac Corporation (FICO), MathWorks, Inc, Teradata, Corporation

Important takeaways from the study:

• The Data Science Platform market report plays host to a superfluity of deliverables which will prove highly beneficial. Say for instance , the report underlines the knowledge concerning market competition trends – highly essential data subject to competitor intelligence and therefore the ongoing Data Science Platform market trends that might enable shareholders to remain competitive and make the foremost of the expansion opportunities prevailing within the Data Science Platform market.
• Another vital takeaway from the report are often credited to the market concentration rate that might aid investors to take a position on the present sales dominance and therefore the plausible trends of the longer term.
• Further deliverables provided within the report include details regarding the sales channels deployed by prominent vendors to retail their stance within the industry. a number of these include direct and indirect marketing.

Table of Contents

1 Data Science Platform Market overview

• Market Introduction
• Research Objectives
• Years Considered
• Market Research Methodology
• Economic Indicators
• Currency Considered

2 Executive Summary

• World Market Overview
• Global Data Science Platform Consumption analysis and forecast
• Data Science Platform Consumption CAGR by Region

3 Market Drivers, Challenges and Trends

• Data Science Platform Market Drivers and Impact
• Growing Demand from Key Regions
• Growing Demand from Key Applications and Potential Industries
• Market Challenges and Impact
• Data Science Platform Market Trends

4 Marketing, Distributors and Customer

• Sales Channel
• Direct Channels
• Indirect Channels

5 Key Players Analysis

• Company Details
• Data Science Platform Product Offered
• Main Business Overview
• Product Benchmarking
• Recent Developments and Technological Advancement

The post Data Science Platform Market Size Detailed Analysis of Current Industry Figures with Forecasts Growth By 2026 appeared first on Artificial Intelligence.

The Deep Learning Market size is expected to grow at an annual average of 38.2% during 2021-2027. DC control tools are systems used to hold torque and automation processes and provide accurate, accurate and repeatable torque control. Torque fixing system manufacturers can optimize the assembly area due to their high performance, reduce labor costs and increase productivity.

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A full report of Deep Learning Market is available at:
https://www.orionmarketreports.com/deep-learning-market-market/46630/

The following segmentation are covered in this report:

By Application
• Image recognition
• Voice recognition
• Video surveillance & diagnostics
• Data mining

By Solution
• Hardware
• Software
• Service

By End-use
• Automotive
• Aerospace & defense
• Healthcare
• Manufacturing
• Others

Company Profiles
• Facebook Inc.
• Google
• Amazon Web Services Inc
• SAS Institute Inc
• Microsoft Corporation
• IBM Corp

Reasons to Buying From us –
• We cover more than 15 major industries, further segmented into more than 90 sectors.
• More than 120 countries are for analysis.
• Over 100+ paid data sources mined for investigation.
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Scope of the report
The research study analyses the Deep Learning Market industry from 360-degree analysis of the market thoroughly delivering insights into the market for better business decisions, considering multiple aspects some of which are listed below as:

Recent developments
• Market overview and growth analysis
• Import and export overview
• Volume analysis
• Current market trends and future outlook
• Market opportunistic and attractive investment segment

Geographic coverage
• North america market size and/or volume
• Latin america market size and/or volume
• Europe market size and/or volume
• Asia-pacific market size and/or volume
• Rest of the world market size and/or volume

Key questions answered by lighting control system market report
• What was the Deep Learning Market size in 2018 and 2019; what are the estimated growth trends and market forecast (2019-2025).
• What will be the cagr of lighting control system market during the forecast period (2019-2025)?
• Which segments (product type/applications/end-user) were most attractive for investments in 2018? how these segments are expected to grow during the forecast period (2019-2025).
• Which manufacturer/vendor/players in the lighting control system market was the market leader in 2018?
• Overview on the existing product portfolio, products in the pipeline, and strategic initiatives taken by key vendors in the market.

The post Deep Learning Market – Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2021-2027 appeared first on Artificial Intelligence.