GLOBAL ARTIFICIAL INTELLIGENCE, ROBOTICS, DATA ANALYTICS MEGA PROJECTS....
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Global Artificial Intelligence (AI) Market Analysis/Opportunity Report 2022 - Transformative Mega Trends in AI Create ICT Growth
Dublin, Sept. 05, 2022 (GLOBE NEWSWIRE) -- The "Global Artificial Intelligence Growth Opportunities" report has been added to ResearchAndMarkets.com's offering.
This research service identifies growth opportunities related to providing integration services to build customized solutions for AI, edge data centers, and offering industry vertical/function-specific applications and others.
As the Mega Trends shape the AI landscape, it will have a ripple effect on new revenue and growth opportunities for start-ups and global information and communication technology (ICT) companies.
RESEARCH HIGHLIGHTS
Artificial intelligence (AI) leverages algorithms and large data sets to find underlying relationships and drive new or better business outcomes. While still nascent, AI technologies are being adopted across industries globally to innovate business models, drive operational efficiencies, and create strategic differentiation.
The COVID-19 pandemic further accelerated the pace of digital transformation and AI adoption as organizations seek to explore new means of creating sustainable business models and driving customer value. In addition, the AI ecosystem is evolving rapidly, making it essential to understand the overarching trends affecting AI and its adoption.
A few trends are:
Key Topics Covered:
1 Strategic Imperatives
2 Growth Environment
3 Growth Opportunity Analysis
4 Way Forward
5 Growth Opportunity Universe
For more information about this report visit https://www.researchandmarkets.com/r/qm59vu
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Artificial Intelligence in Retail Market Report 2022-2030, Industry Growth, Analysis Key Players, Share, Size, and Forecast
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Sep 09, 2022 (Alliance News via COMTEX) -- Report Ocean recently added a research report on Artificial Intelligence in Retail Market. The report includes an extensive analysis of the market's characteristics, COVID-19 impact, size and growth, segmentation, regional and country breakdowns, competitive environment, market shares, trends, and strategies. In addition, it traces the development of the market over time and projects regional market growth. It compares the market to other markets and situates it in relation to the larger market.
The globalis Artificial Intelligence in Retail Market expected to reach US$ Million by 2027, with a CAGR of $$% from 2020 to 2027, based on Report Ocean newly published report. The demand for Internet-of-Things (IoT) technology and services are growing globally, especially around applications within the healthcare, energy, transport, public sector, and manufacturing industries. Many countries have led to the emergence of IoT/smart city projects.
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Driving FactorsTypically, the ICT market includes software, hardware, and services that are pertinent to telephone and computer network technology. The ICT market's largest sector is telecommunications, while the fastest-growing sector is recent technologies, which include AR/VR and robots. Notably, future investments will also be made in next-generation security technologies that offer integrated security solutions.
As of 2022 data, the United States held over ~36% of the global market share for information and communication technology (ICT). With a market share of 16%, the EU ranked second, followed by 12%, and China ranked third.
In addition, according to forecasts, the ICT market will reach more than US$ 6 trillion in 2022 and almost US$ 7 trillion by 2030. Over the next few years, traditional tech spending will be driven mainly by big data and analytics, mobile, social, and cloud computing.
The prime objective of this report is to provide the insights on the post COVID-19 impact which will help market players in this field evaluate their business approaches. Also, this report covers market segmentation by major market verdors, types, applications/end users and geography(North America, East Asia, Europe, South Asia, Southeast Asia, Middle East, Africa, Oceania, South America).
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By Market Verdors:
By Types:
Cloud
On-Premises
By Applications:
Predictive Merchandising
Programmatic Advertising
Market Forecasting
In-Store Visual Monitoring and Surveillance
Location-Based Marketing
Others
Table of Content:
Market IntroductionMarket Report Scope and MethodologyOverview of Market Research MethodologyMarket Overview and DynamicsMarket Revenue Share Analysis, By Key PlayersMarket Segmentation By Application, By TypeMarket COVID-19 Impact analysis with the Impact of COVID-19Market Competitive Landscape AnalysisMarket by Region, Historical Data and Market ForecastsMarket Conclusion
Continued...................
Key Indicators Analysed
Market Players & Competitor Analysis: The report covers the key players of the industry including Company Profile, Product Specifications, Production Capacity/Sales, Revenue, Price and Gross Margin 2016-2027 & Sales with a thorough analysis of the markets competitive landscape and detailed information on vendors and comprehensive details of factors that will challenge the growth of major market vendors.
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Global and Regional Market Analysis: The report includes Global & Regional market status and outlook 2016-2027. Further the report provides break down details about each region & countries covered in the report. Identifying its sales, sales volume & revenue forecast. With detailed analysis by types and applications.
Market Trends:Market key trends which include Increased Competition and Continuous Innovations.
Opportunities and Drivers:Identifying the Growing Demands and New Technology
Porters Five Force Analysis: The report provides with the state of competition in industry depending on five basic forces: threat of new entrants, bargaining power of suppliers, bargaining power of buyers, threat of substitute products or services, and existing industry rivalry.
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Artificial Intelligence (AI) Robots Market Size Is Expected To Grow At A CAGR Of 21.3% During Assessment Period 2022-2028 | 122 Report Pages
The MarketWatch News Department was not involved in the creation of this content.
Sep 05, 2022 (The Expresswire) -- "Artificial Intelligence (AI) Robots Market" Insights 2022 By Types (Service, Industria), Applications (Public Relations, StocKManagement), Regions and Forecast to 2028. The global Artificial Intelligence (AI) Robots market size is projected to reach multi million by 2028, in comparison to 2022, with unexpected CAGR during the forecast period, the Artificial Intelligence (AI) Robots Market Report Contains 122 pages Including Full TOC, Tables and Figures, and Chart with In-depth Analysis Pre and Post COVID-19 Market Outbreak Impact Analysis and Situation by Region.
Artificial Intelligence (AI) Robots Market - Covid-19 Impact and Recovery Analysis:We have been tracking the direct impact of COVID-19 on this market, as well as the indirect impact from other industries. This report analyzes the impact of the pandemic on the Artificial Intelligence (AI) Robots market from a Global and Regional perspective. The report outlines the market size, market characteristics, and market growth for Artificial Intelligence (AI) Robots industry, categorized by type, application, and consumer sector. In addition, it provides a comprehensive analysis of aspects involved in market development before and after the Covid-19 pandemic. Report also conducted a PESTEL analysis in the industry to study key influencers and barriers to entry.
Final Report will add the analysis of the impact of COVID-19 on this industry.
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It also provides accurate information and cutting-edge analysis that is necessary to formulate an ideal business plan, and to define the right path for rapid growth for all involved industry players. With this information, stakeholders will be more capable of developing new strategies, which focus on market opportunities that will benefit them, making their business endeavours profitable in the process.
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Artificial Intelligence (AI) Robots Market - Competitive and Segmentation Analysis:This Artificial Intelligence (AI) Robots Market report offers detailed analysis supported by reliable statistics on sale and revenue by players for the period 2017-2022. The report also includes company description, major business, Artificial Intelligence (AI) Robots product introduction, recent developments and Artificial Intelligence (AI) Robots sales by region, type, application and by sales channel.
The major players covered in the Artificial Intelligence (AI) Robots market report are:
● ABB● Alphabet● Amazon● Asustek Computer● Blue Frog Robotics● Bsh Hausgeräte● Fanuc● Hanson Robotics● Harman International Industries● IBM● Intel● Jibo● Kuka● LG● Mayfield Robotics● Microsoft● Neurala● Nvidia● Promobot● Softbank● Xilinx
Short Summery About Artificial Intelligence (AI) Robots Market :
The Global Artificial Intelligence (AI) Robots market is anticipated to rise at a considerable rate during the forecast period, between 2022 and 2028. In 2021, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.
The Artificial Intelligence (AI) Robots market report provides a detailed analysis of global market size, regional and country-level market size, segmentation market growth, market share, competitive Landscape, sales analysis, impact of domestic and global market players, value chain optimization, trade regulations, recent developments, opportunities analysis, strategic market growth analysis, product launches, area marketplace expanding, and technological innovations.According to our (Global Info Research) latest study, due to COVID-19 pandemic, the global Artificial Intelligence (AI) Robots market size is estimated to be worth USD 6864.4 million in 2021 and is forecast to a readjusted size of USD 26480 million by 2028 with a CAGR of 21.3% during review period. Public Relations accounting for % of the Artificial Intelligence (AI) Robots global market in 2021, is projected to value USD million by 2028, growing at a % CAGR in next six years. While Service segment is altered to a % CAGR between 2022 and 2028.Global key manufacturers of Artificial Intelligence (AI) Robots include ABB, Alphabet, Amazon, Asustek Computer, and Blue Frog Robotics, etc. In terms of revenue, the global top four players hold a share over % in 2021.
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Report further studies the market development status and future Artificial Intelligence (AI) Robots Market trend across the world. Also, it splits Artificial Intelligence (AI) Robots market Segmentation by Type and by Applications to fully and deeply research and reveal market profile and prospects.
On the basis of product typethis report displays the production, revenue, price, market share and growth rate of each type, primarily split into:
● Service● Industria
On the basis of the end users/applicationsthis report focuses on the status and outlook for major applications/end users, consumption (sales), market share and growth rate for each application, including:
● Public Relations● StocKManagement
Artificial Intelligence (AI) Robots Market - Regional Analysis:
Geographically, this report is segmented into several key regions, with sales, revenue, market share and growth Rate of Artificial Intelligence (AI) Robots in these regions, from 2015 to 2027, covering
● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam) ● South America (Brazil, Argentina, Columbia etc.) ● Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)Some of the key questions answered in this report:
● What is the global (North America, Europe, Asia-Pacific, South America, Middle East and Africa) sales value, production value, consumption value, import and export of Artificial Intelligence (AI) Robots? ● Who are the global key manufacturers of the Artificial Intelligence (AI) Robots Industry? How is their operating situation (capacity, production, sales, price, cost, gross, and revenue)? ● What are the Artificial Intelligence (AI) Robots market opportunities and threats faced by the vendors in the global Artificial Intelligence (AI) Robots Industry? ● Which application/end-user or product type may seek incremental growth prospects? What is the market share of each type and application? ● What focused approach and constraints are holding the Artificial Intelligence (AI) Robots market? ● What are the different sales, marketing, and distribution channels in the global industry? ● What are the upstream raw materials and manufacturing equipment of Artificial Intelligence (AI) Robots along with the manufacturing process of Artificial Intelligence (AI) Robots? ● What are the key market trends impacting the growth of the Artificial Intelligence (AI) Robots market? ● Economic impact on the Artificial Intelligence (AI) Robots industry and development trend of the Artificial Intelligence (AI) Robots industry. ● What are the market opportunities, market risk, and market overview of the Artificial Intelligence (AI) Robots market? ● What are the key drivers, restraints, opportunities, and challenges of the Artificial Intelligence (AI) Robots market, and how they are expected to impact the market? ● What is the Artificial Intelligence (AI) Robots market size at the regional and country-level?Our research analysts will help you to get customized details for your report, which can be modified in terms of a specific region, application or any statistical details. In addition, we are always willing to comply with the study, which triangulated with your own data to make the market research more comprehensive in your perspective.
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Detailed TOC of Global Artificial Intelligence (AI) Robots Market Research Report 20221 Artificial Intelligence (AI) Robots Market Overview
1.1 Product Overview and Scope of Artificial Intelligence (AI) Robots1.2 Artificial Intelligence (AI) Robots Segment by Type1.2.1 Global Artificial Intelligence (AI) Robots Market Size Growth Rate Analysis by Type 2022 VS 20281.3 Artificial Intelligence (AI) Robots Segment by Application1.3.1 Global Artificial Intelligence (AI) Robots Consumption Comparison by Application: 2022 VS 20281.4 Global Market Growth Prospects1.4.1 Global Artificial Intelligence (AI) Robots Revenue Estimates and Forecasts (2017-2028)1.4.2 Global Artificial Intelligence (AI) Robots Production Capacity Estimates and Forecasts (2017-2028)1.4.3 Global Artificial Intelligence (AI) Robots Production Estimates and Forecasts (2017-2028)1.5 Global Market Size by Region1.5.1 Global Artificial Intelligence (AI) Robots Market Size Estimates and Forecasts by Region: 2017 VS 2021 VS 20281.5.2 North America Artificial Intelligence (AI) Robots Estimates and Forecasts (2017-2028)1.5.3 Europe Artificial Intelligence (AI) Robots Estimates and Forecasts (2017-2028)1.5.4 China Artificial Intelligence (AI) Robots Estimates and Forecasts (2017-2028)1.5.5 Japan Artificial Intelligence (AI) Robots Estimates and Forecasts (2017-2028)
2 Market Competition by Manufacturers2.1 Global Artificial Intelligence (AI) Robots Production Capacity Market Share by Manufacturers (2017-2022)2.2 Global Artificial Intelligence (AI) Robots Revenue Market Share by Manufacturers (2017-2022)2.3 Artificial Intelligence (AI) Robots Market Share by Company Type (Tier 1, Tier 2 and Tier 3)2.4 Global Artificial Intelligence (AI) Robots Average Price by Manufacturers (2017-2022)2.5 Manufacturers Artificial Intelligence (AI) Robots Production Sites, Area Served, Product Types2.6 Artificial Intelligence (AI) Robots Market Competitive Situation and Trends2.6.1 Artificial Intelligence (AI) Robots Market Concentration Rate2.6.2 Global 5 and 10 Largest Artificial Intelligence (AI) Robots Players Market Share by Revenue2.6.3 Mergers and Acquisitions, Expansion
3 Production Capacity by Region3.1 Global Production Capacity of Artificial Intelligence (AI) Robots Market Share by Region (2017-2022)3.2 Global Artificial Intelligence (AI) Robots Revenue Market Share by Region (2017-2022)3.3 Global Artificial Intelligence (AI) Robots Production Capacity, Revenue, Price and Gross Margin (2017-2022)3.4 North America Artificial Intelligence (AI) Robots Production3.4.1 North America Artificial Intelligence (AI) Robots Production Growth Rate (2017-2022)3.4.2 North America Artificial Intelligence (AI) Robots Production Capacity, Revenue, Price and Gross Margin (2017-2022)3.5 Europe Artificial Intelligence (AI) Robots Production3.5.1 Europe Artificial Intelligence (AI) Robots Production Growth Rate (2017-2022)3.5.2 Europe Artificial Intelligence (AI) Robots Production Capacity, Revenue, Price and Gross Margin (2017-2022)3.6 China Artificial Intelligence (AI) Robots Production3.6.1 China Artificial Intelligence (AI) Robots Production Growth Rate (2017-2022)3.6.2 China Artificial Intelligence (AI) Robots Production Capacity, Revenue, Price and Gross Margin (2017-2022)3.7 Japan Artificial Intelligence (AI) Robots Production3.7.1 Japan Artificial Intelligence (AI) Robots Production Growth Rate (2017-2022)3.7.2 Japan Artificial Intelligence (AI) Robots Production Capacity, Revenue, Price and Gross Margin (2017-2022)
4 Global Artificial Intelligence (AI) Robots Consumption by Region4.1 Global Artificial Intelligence (AI) Robots Consumption by Region4.1.1 Global Artificial Intelligence (AI) Robots Consumption by Region4.1.2 Global Artificial Intelligence (AI) Robots Consumption Market Share by Region4.2 North America4.2.1 North America Artificial Intelligence (AI) Robots Consumption by Country4.2.2 United States4.2.3 Canada4.3 Europe4.3.1 Europe Artificial Intelligence (AI) Robots Consumption by Country4.3.2 Germany4.3.3 France4.3.4 U.K.4.3.5 Italy4.3.6 Russia4.4 Asia Pacific4.4.1 Asia Pacific Artificial Intelligence (AI) Robots Consumption by Region4.4.2 China4.4.3 Japan4.4.4 South Korea4.4.5 China Taiwan4.4.6 Southeast Asia4.4.7 India4.4.8 Australia4.5 Latin America4.5.1 Latin America Artificial Intelligence (AI) Robots Consumption by Country4.5.2 Mexico4.5.3 Brazil
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5 Segment by Type5.1 Global Artificial Intelligence (AI) Robots Production Market Share by Type (2017-2022)5.2 Global Artificial Intelligence (AI) Robots Revenue Market Share by Type (2017-2022)5.3 Global Artificial Intelligence (AI) Robots Price by Type (2017-2022)6 Segment by Application6.1 Global Artificial Intelligence (AI) Robots Production Market Share by Application (2017-2022)6.2 Global Artificial Intelligence (AI) Robots Revenue Market Share by Application (2017-2022)6.3 Global Artificial Intelligence (AI) Robots Price by Application (2017-2022)
7 Key Companies Profiled7.1 Company7.1.1 Artificial Intelligence (AI) Robots Corporation Information7.1.2 Artificial Intelligence (AI) Robots Product Portfolio7.1. CArtificial Intelligence (AI) Robots Production Capacity, Revenue, Price and Gross Margin (2017-2022)7.1.4 Company’s Main Business and Markets Served7.1.5 Company’s Recent Developments/Updates
8 Artificial Intelligence (AI) Robots Manufacturing Cost Analysis8.1 Artificial Intelligence (AI) Robots Key Raw Materials Analysis8.1.1 Key Raw Materials8.1.2 Key Suppliers of Raw Materials8.2 Proportion of Manufacturing Cost Structure8.3 Manufacturing Process Analysis of Artificial Intelligence (AI) Robots8.4 Artificial Intelligence (AI) Robots Industrial Chain Analysis
9 Marketing Channel, Distributors and Customers9.1 Marketing Channel9.2 Artificial Intelligence (AI) Robots Distributors List9.3 Artificial Intelligence (AI) Robots Customers
10 Market Dynamics10.1 Artificial Intelligence (AI) Robots Industry Trends10.2 Artificial Intelligence (AI) Robots Market Drivers10.3 Artificial Intelligence (AI) Robots Market Challenges10.4 Artificial Intelligence (AI) Robots Market Restraints
11 Production and Supply Forecast11.1 Global Forecasted Production of Artificial Intelligence (AI) Robots by Region (2022-2028)11.2 North America Artificial Intelligence (AI) Robots Production, Revenue Forecast (2022-2028)11.3 Europe Artificial Intelligence (AI) Robots Production, Revenue Forecast (2022-2028)11.4 China Artificial Intelligence (AI) Robots Production, Revenue Forecast (2022-2028)11.5 Japan Artificial Intelligence (AI) Robots Production, Revenue Forecast (2022-2028)
12 Consumption and Demand Forecast12.1 Global Forecasted Demand Analysis of Artificial Intelligence (AI) Robots12.2 North America Forecasted Consumption of Artificial Intelligence (AI) Robots by Country12.3 Europe Market Forecasted Consumption of Artificial Intelligence (AI) Robots by Country12.4 Asia Pacific Market Forecasted Consumption of Artificial Intelligence (AI) Robots by Region12.5 Latin America Forecasted Consumption of Artificial Intelligence (AI) Robots by Country
13 Forecast by Type and by Application (2022-2028)13.1 Global Production, Revenue and Price Forecast by Type (2022-2028)13.1.1 Global Forecasted Production of Artificial Intelligence (AI) Robots by Type (2022-2028)13.1.2 Global Forecasted Revenue of Artificial Intelligence (AI) Robots by Type (2022-2028)13.1.3 Global Forecasted Price of Artificial Intelligence (AI) Robots by Type (2022-2028)13.2 Global Forecasted Consumption of Artificial Intelligence (AI) Robots by Application (2022-2028)13.2.1 Global Forecasted Production of Artificial Intelligence (AI) Robots by Application (2022-2028)13.2.2 Global Forecasted Revenue of Artificial Intelligence (AI) Robots by Application (2022-2028)13.2.3 Global Forecasted Price of Artificial Intelligence (AI) Robots by Application (2022-2028)
14 Research Finding and Conclusion
15 Methodology and Data Source15.1 Methodology/Research Approach15.1.1 Research Programs/Design15.1.2 Market Size Estimation15.1.3 Market Breakdown and Data Triangulation15.2 Data Source15.2.1 Secondary Sources15.2.2 Primary Sources15.3 Author List15.4 Disclaimer
Continued….
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How Colleges Are Using Artificial Intelligence To Improve Enrollment And Retention
More colleges are using artificial intelligence to increase their enrollments, target financial aid ... [+] and improve retention rates.
gettyArtificial intelligence (AI) has gradually become accepted by colleges and universities as an effective tool for automating a number of tasks effectively and efficiently. Chatbots can answer students’ questions about class scheduling or check in with them about their mental health. AI-generated emails can remind students about important deadlines, prompt them to register for classes, turn in assignments and pay their fees on time. And, in a particularly controversial use, AI-based software is increasingly able to detect plagiarized assignments.
One professor at Georgia Tech, even used AI to build a virtual teaching assistant, called Jill Watson. Turns out that “Jill” receives very positive student evaluations.
Higher education is advancing from its initial forays into digital transformation that involved automating daily tasks, digitizing workflows, developing more complex datasets, and creating dashboards to improve their analytics. Now, institutions are not simply using technology to do the same things better. They’re deploying AI to do better things.
College leaders have learned that AI can do more than merely churn out routine prompts and generate helpful tips. They’re starting to use the technology to address some of their largest and most persistent challenges - including such bottom-line issues as increasing enrollment, improving student retention, and allocating financial aid.
And as AI expands into these core university practices, new concerns are also being raised about the tool’s threats to personal privacy and its vulnerability to systematic bias.
According to Arijit Sengupta, founder of Aible, a San Francisco-based AI company, colleges and universities are starting to catch up with other industries like banking and healthcare in using AI to impact key performance indicators.
Sengupta told me in a recent interview that he now has somewhere between 5-10 higher education clients who are using AI to help them make progress on key outcomes like increasing their yield from applicants, preventing first-to-second year attrition, targeting institutional financial aid and optimizing the solicitation of alumni donors.
Sengupta knows that university leaders have often been disappointed with the results of previous AI projects, and he agrees that in many cases AI is a waste of time and money because it’s not built to achieve tangible goals and specific outcomes that are most important to the institution.
With that in mind, Sengupta offers his clients the following guarantee - if Aible’s AI models and prescribed interventions don’t produce value in the first 30 days, the client won’t be charged. He told me that many college officials believe they need to understand AI logarithms and models before they can apply them, but according to Sengupta, they have it all wrong. “Our approach is to teach AI to ‘speak human,’ rather than the other way around.”
Once an AI model sorts through the complexity of a large amount of data and detects previously hidden patterns, the focus needs to become “what do we do about it - in other words, whom do we need to target, with what intervention, and when.” That’s where colleges tend to get bogged down says Sengupta; “their computer experts search for the perfect algorithm, rather than focusing on how to best change their practices to take advantage of what machine learning has provided them in the way of predictions and recommendations.”
As an example, one private, mid-sized university wanted to increase the percentage of applicants who eventually matriculated at the university. It was spending thousands of dollars to purchase student prospect lists, and devoting hundreds of hours calling the students on those lists. But the end result was disappointing - fewer than 10% of the applicants ever officially enrolled in the university.
Instead of “carpet bombing” all the names on the list, Aible was able to generate a model that guided the university toward much more precise targeting of students. It identified a subset of applicants who - based on their demographic characteristics, income levels and family history of attending college - were most likely to respond to well-timed phone calls from the faculty. It also identified the amount of financial aid that it would take to influence their enrollment decision.
It then advised the university to make personal calls to those students along with the tailored financial aid offers. The time this intervention took - from identifying and collecting the relevant data, developing the algorithm and recommending the intervention strategy - took about three weeks. The preliminary results indicate that the university will likely see about a 15% increase in its enrollment yield.
When Nova Southeastern University in Ft. Lauderdale, Florida, wanted to use its data to improve undergraduate retention, it used an Aible solution to identify the students who were most likely to leave. This helped the university’s center for academic and student achievement target and prioritize its retention efforts for the most at-risk students.
While most retention efforts are reactionary - activated only after finding a warning sign that a student is in academic peril - an effective AI strategy should help a college target curricular changes, intensify its advising and offer support services much earlier, before the time when a student begins to experience troubles.
One thing I discovered in researching this article is that colleges are often reluctant to acknowledge they’re using AI for purposes like these, insisting on remaining anonymous in press accounts. That concern did not surprise Sengupta who believes it’s due to the belief that using AI increases the risk that a person’s privacy will be violated.
One way to make sure individual privacy is protected is to maintain all the data on the university’s rather than on a vendor’s servers. Another is to not use information on groups smaller than 25 so that individual information cannot be inferred.
Hernan Londono, Senior Strategist for Higher Education at Dell Technologies, believes that privacy worries are not the only reason universities might hesitate to employ AI and skittish about admitting it when they do. “AI-based interventions may be biased because various populations of students may be differentially excluded from the data,” he told me.
Not only does AI reflect human biases, it can amplify them by feeding nonrepresentaive data to the algorithms, which then are used to drive important decisions. As one example, Amazon stopped using a hiring algorithm after discovering that it favored applicants based on words like “executed” or “captured” that were more common on men’s versus women’s resumes.
As significant as concerns about privacy and bias may be, colleges are inevitably going to increase their reliance on AI. It’s too powerful a tool to just sit on the higher education shelf. Its applications will continue to grow, and with the proper controls and precautions, it can be used to improve college performance and promote student success at the same time.
10 top artificial intelligence (AI) applications in healthcare
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Artificial intelligence (AI) is being applied across the healthcare spectrum — from administration to patient interaction and medical research, diagnosis and treatment.
What is healthcare AI?Healthcare AI is the application of artificial intelligence to medical services and the administration or delivery of medical services. Machine learning (ML), large and often unstructured datasets, advanced sensors, natural language processing (NLP) and robotics are all being used in a growing number of healthcare sectors.
Along with great promise, the technology offers significant potential concerns — including the abuse that can come from the centralization and digitalization of patient data as well as possible linkages with nanomedicine or universal biometric IDs. Equity and bias have both also been concerns in some early AI applications, but the technology may also be able to improve healthcare equity.
Although deployment of AI in the healthcare sector has truly just begun, it is becoming more commonly used. Gartner pegged 2021 global healthcare IT spending at $140 billion, with enterprises listing AI and robotic process automation (RPA) as their lead spending priorities.
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Register HereHealthcare costs approached a fifth (19.7%) of the total U.S. economy in 2020 (an estimated 19.7% or $4.1 trillion). Over half of that spending, for the first time, was racked up by the government, where fraud is especially high.
Thus, the potential value of healthcare AI, from administration to medical AI is vast.
10 top applications of artificial intelligence in healthcare in 2022Here are 10 of the top areas where healthcare AI use cases are being developed and deployed today.
1. Healthcare administrationAdministrative expenses are estimated to comprise 15% to 25% of total healthcare costs. Tools to improve and streamline administration are valuable for insurers, payers and providers alike.
Identifying and cutting down fraud, however, may provide the most immediate return as ealthcare fraud can happen on many levels and be committed by various parties. In some of the worst cases, fraud may cause insurers to get billed for services not rendered or result in surgeons performing unnecessary operations to get higher insurance payments. Insurers may also get billed for defective devices or test kits.
AI can be a useful tool in stopping fraud before it happens. Just as banks commonly use algorithms to detect unusual transactions, and health insurers can do the same..
2. Public healthAI is already being applied across the public health sector. Including
The applications for AI in medical research are also expansive. Examples range from new and repurposed drug discovery to clinical trials, including:
AI may also alter how medical school students receive parts of their education. Including in cases like the following:
AI is also deployed to support medical professionals in clinical settings, including the following:
AI is also deployed to support patients directly:
Telemedicine in the form of virtual doctor visits have become increasingly common since the COVID-19 lockdowns. In addition to those, AI is supporting other forms of remote medicine as well, including:
AI is also utilized for healthcare center diagnostics, including by:
AI does not eliminate surgical issues, but it can potentially reduce them while enhancing outcomes for patients and surgeons alike. This is illustrated in the following examples
Along with the above-described diagnostic use cases, clinicians also must meet patients physical needs and, more prosaically, stock supplies and deliver goods. AI-powered collaborative robots are starting to ease the burden. Gartner expects 50% of U.S. providers to invest in robotics process automation (RPA) by 2023. Some examples of RPA in hospitals include:
Atheon provides robots that support not only medical functions, but tasks such as linen distribution and waste removal.
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Artificial Intelligence Will Change Jobs—For the Better
The ramifications of advances in artificial intelligence (A.I.) are being felt further afield than anyone expected. A.I. perhaps entered the public consciousness in the 1990s thanks to chess competitions, but it's now infiltrating art competitions and, soon, the written word. Some commercial offerings can provide paragraphs of text based on brief prompts, keywords, and tone parameters. Users of Google's email service have, of course, been microdosing on A.I. since 2018, when Gmail rolled out Smart Compose.
What these developments bring home is that people in the so-called "creative class" are now facing the first-person reckoning that automation has long presented to blue-collar workers: Technology is going to radically change the way we work.
As an analyst at a think tank, my job consists of processing policy trends, formulating new ideas to tackle economic and social problems, and advancing them through the written word. If programs like Midjourney, DALL-E, and Voyager can already captivate human audiences, I haven't the slightest doubt that my modest ability to metabolize the policy landscape, reason my way to novel solutions, and manipulate language in provocative, engaging ways will soon be matched—and then surpassed—by A.I. programs designed for the task.
While I am under no illusion that my work merits any blue ribbons, putting thoughts into words that persuade or stir emotion entails a certain artistry. It's an engrossing and gratifying process, one from which I derive identity. When I contemplate that a computer could soon do it better, I, like the Lancashire handloom weavers of the early 19th century, feel more than a bit threatened.
Garry Kasparov dealt with this conundrum two decades ago and has had a head start in managing the prospect of obsolescence. Kasparov, an all-time great chess player, had the distinction of holding the world title just at the same moment that computer chess programs ramped up their prowess. In 1996, Kasparov beat what was then the strongest chess engine ever created, IBM's Deep Blue. But as he recounts in his memoir, Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, he knew then that his reign would soon end. Indeed, in a 1997 rematch for which Kasparov was handsomely compensated, an updated Deep Blue brought the age of A.I. to global attention, dealing the champion a stunning defeat in the match's decisive sixth game.
In Deep Thinking, published in 2017, Kasparov explains how his perspective on A.I. has evolved and why. Despite the anguish the 1997 loss caused him, he views A.I. as one of the greatest opportunities for humanity to advance its well-being. The reason is that Kasparov has observed in the intervening years that the highest level of performance, on the chessboard and elsewhere, is reached when humans work with smart machines.
After Deep Blue's programmers established that it could see deeper into the game than the human mind, Kasparov and a group of partners came up with a new concept: What if instead of human vs. machine, people played against one another but with the assistance of chess software?
They called the new style of play "advanced chess," and the outcomes surprised Kasparov. It wasn't the player with the best chess software that necessarily won, nor was it the best human player. Rather, the top performers were the players who were able to use the machines most effectively, those who were able to get the most out of the chess engines and their own creative abilities.
Operating on the premise of Moravec's Paradox, i.e., where machines are strong is where humans are weak and vice versa, what Kasparov took away from the advanced chess experiment is that a clever working process beats both superior human talent and superior technological horsepower.
The same insight can be leveraged by artists, composers, writers, designers, and the like. Rather than viewing A.I. as the end of our livelihoods, we ought to see the opportunities it presents for better work.
For the creative class, the answer to the A.I. challenge is to make the most of the programs available to us. Is artistry lost because of A.I., or is it unlocked, as we are freed from some of the more formulaic structuring processes that drain energy? By delegating these aspects of creation to A.I., I anticipate having more mental space available to generate the rhetorical flourishes and the witty bits of embroidery that make writing enjoyable.
Yes, people deploying A.I. in the writing world, art competitions, and elsewhere will likely face scorn. But while a level playing field is appropriate in defined competitions, in open-ended fields to accuse a rival of cheating would be no more meaningful than in that of the textile industry. For the intrepid writer, A.I. will create opportunities to produce better work at a faster clip, just as the power loom did for the weavers of Lancashire.
Rather than fear, and certainly rather than Luddite suppression, this ought to be a moment of optimism. A.I. is coming for our jobs. Its arrival, however, will not be a harbinger of obsolescence but a catalyst for greater achievement.
Show, don’t tell: Tips for robotics startups raising a Series B during a downturn
Jason Schoettler Contributor
Raising a Series B for any startup is challenging right now, with many VCs pulling back on investments — funding for Series B rounds across all sectors fell 55% in August compared to a year earlier, for example.
But raising a Series B for a hardware startup can be even tougher. It has simply always been more difficult to get venture investors to fund a robotics project compared to a software-only venture, given robotics’ high capital requirements and the greater risk.
However, the climb uphill can get much easier if a robotics startup can showcase a solid business model, measurable metrics and a plan for the next 18 months. As an investor in AI and automation companies for over 20 years, I’ve backed dozens of robotics companies, and I continue to be bullish on the space.
You need to show that customers are deriving real value from your robots — saving time, money or both.
Here are several strategies founders can use to prepare their robotics companies for a successful Series B.
Show how your robot worksRobots are inherently visual (can anyone forget that video of Boston Dynamics robots dancing?) So when you pitch VCs on your automation company, it pays to demonstrate your robots in action.
If your robots are large installations in warehouses or on manufacturing lines, invite VCs to come to see them working. If they are small enough to transport, bring them with you to the pitch meeting. And always have high-quality video available to share on a computer or tablet during in-person pitches or online for virtual meetings. Seeing your product in action is critical to getting investors excited about it.
Show customer ROIShow, don’t tell: Tips for robotics startups raising a Series B during a downturn by Ram Iyer originally published on TechCrunch
Tesla Robot Shows Some Skills, But Falls Flat With Some Robotics Insiders
An early prototype of Tesla Inc.'s proposed Optimus humanoid robot slowly and awkwardly walked onto a stage, turned, and waved to a cheering crowd at the company's artificial intelligence event Friday.
But the basic tasks by the robot with exposed wires and electronics — as well as a later, next generation version that had to be carried onstage by three men — was a long way from CEO Elon Musk's vision of a human-like robot that can change the world.
Musk told the crowd, many of whom might be hired by Tesla, that the robot can do much more than the audience saw Friday. He said it is also delicate and “we just didn’t want it to fall on its face."
Musk suggested that the problem with flashy robot demonstrations is that the robots are “missing a brain” and don’t have the intelligence to navigate themselves, but he gave little evidence Friday that Optimus was any more intelligent than robots developed by other companies and researchers.
The demo didn’t impress AI researcher Filip Piekniewski, who tweeted it was “next level cringeworthy” and a “complete and utter scam.” He said it would be “good to test falling, as this thing will be falling a lot.”
“None of this is cutting edge,” tweeted robotics expert Cynthia Yeung. “Hire some PhDs and go to some robotics conferences @Tesla.”
Yeung also questioned why Tesla opted for its robot to have a human-like hand with five fingers, noting “there’s a reason why” warehouse robots developed by startup firms use pinchers with two or three fingers.
Musk said that Friday night was the first time the early robot walked onstage without a tether. Tesla's goal, he said, is to make an “extremely capable” robot in high volumes — possibly millions of them — at a cost that could be less than a car, that he guessed would be less than $20,000.
Tesla showed a video of the robot, which uses artificial intelligence that Tesla is testing in its “Full Self-Driving” vehicles, carrying boxes and placing a metal bar into what appeared to be a factory machine. But there was no live demonstration of the robot completing the tasks.
Employees told the crowd in Palo Alto, California, as well as those watching via livestream, that they have been working on Optimus for six to eight months. People can probably buy an Optimus “within three to five years," Musk said.
Employees said Optimus robots would have four fingers and a thumb with a tendon-like system so they could have the dexterity of humans.
The robot is backed by giant artificial intelligence computers that track millions of video frames from “Full Self-Driving” autos. Similar computers would be used to teach tasks to the robots, they said.
Experts in the robotics field were skeptical that Tesla is anywhere near close to rolling out legions of human-like home robots that can do the “useful things” Musk wants them to do – say, make dinner, mow the lawn, keep watch on an aging grandmother.
“When you’re trying to develop a robot that is both affordable and useful, a humanoid kind of shape and size is not necessarily the best way,” said Tom Ryden, executive director of the nonprofit startup incubator Mass Robotics.
Tesla isn’t the first car company to experiment with humanoid robots.
Honda more than two decades ago unveiled Asimo, which resembled a life-size space suit and was shown in a carefully-orchestrated demonstration to be able to pour liquid into a cup. Hyundai also owns a collection of humanoid and animal-like robots through its 2021 acquisition of robotics firm Boston Dynamics. Ford has partnered with Oregon startup Agility Robotics, which makes robots with two legs and two arms that can walk and lift packages.
Ryden said carmakers’ research into humanoid robotics can potentially lead to machines that can walk, climb and get over obstacles, but impressive demos of the past haven't led to an “actual use scenario” that lives up to the hype.
“There’s a lot of learning that they’re getting from understanding the way humanoids function,” he said. “But in terms of directly having a humanoid as a product, I’m not sure that that’s going to be coming out anytime soon.”
Critics also said years ago that Musk and Tesla wouldn't be able to build a profitable new car company that used batteries for power rather than gasoline.
Tesla is testing “Full Self-Driving” vehicles on public roads, but they have to be monitored by selected owners who must be ready to intervene at all times. The company says it has about 160,000 vehicles equipped with the test software on the road today.
Critics have said the Teslas, which rely on cameras and powerful computers to drive by themselves, don't have enough sensors to drive safely. Tesla's less capable Autopilot driver-assist system, with the same camera sensors, is under investigation by U.S. safety regulators for braking for no reason and repeatedly running into emergency vehicles with flashing lights parked along freeways.
In 2019, Musk promised a fleet of autonomous robotaxis would be in use by the end of 2020. They are still being tested.
Researchers at MIT and Brigham and Women's Hospital found that patients didn't mind receiving care from a Boston Dynamics robot dog affixed with a tablet, on a video call with a human doctor. An author of the study tells LX News about how the experiment went.
First-Ever Spinal Surgical Hand-held Robot by Point Robotics MedTech to Make Its Worldwide Debut in the United States
TAIPEI, Sept. 28, 2022 /PRNewswire/ -- Point Robotics MedTech Inc. (Point Robotics), a rising star in the field of orthopedic surgery, received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its minimally invasive surgical robot, POINT™ Kinguide Robotic-Assisted Surgical System, in August. This premarket notification marks both Taiwan's very first FDA-cleared surgical robot and the first ever hand-held robot framework equipped with a parallel manipulator for orthopedic application in the world.
An integrated surgical system including both image-guided navigation and hand-held drilling features, the "Kinguide Robotic-Assisted Surgical System" streamlines procedural tasks and considerably reduces surgeon's burdens during spinal fusion surgeries, and its outstanding performance on precision, stability, and reproducibility of robot motion can improve the surgical outcomes which used to strongly rely on the experience of the surgeon. The major difference between Point Robotics' product and the others is the expandability of the indication for more complicated Herniated Disc Decompression surgery enabled by the unique parallel manipulator mechanism.
Point Robotics anticipates diving into the blue ocean market by addressing the even bigger unmet clinical needs with its next-gen products. "Point Robotics is revolutionizing spinal surgery by combining the clinical know-how garnered from years of surgeons' experience and cutting-edge technologies." Said Jackie Yang, the Co-Founder of Translink Capital in Silicon Valley.
Point Robotics is preparing for CE marking in Europe and registration certificate in China to jump start global deployments and to access international markets. According to Frost & Sullivan, the global market size of orthopedic surgical robots is expected to reach nearly US$8 billion by 2030 at a CAGR of nearly 20%, which indicates a promising market potential driven by the increasing demand for surgical robots in light of their clinical benefits and breakthrough in key technologies. To ride the wave of strong market momentum, "It's our honor to make the worldwide debut of the cutting-edge robot system starting from the world's largest surgical robot market - the United States. We aim to promote availability and affordability of robot's adoption for spinal surgery unaddressed by today's technology to treat more patients who contracted spectrum of spinal diseases." said SC Juang, CEO of Point Robotics. In prospect, Point Robotics is planning to serve various countries through engaging strategic partners and business models, such as joint ventures, licensing, or strategic alliances, etc.
To further reveal the potential needs in markets, Point Robotics has been conducting a month-long product demonstration for surgeons on the US West Coast since August, and will make the "Kinguide Robotic-Assisted Surgical System" debut at the following medical conferences: SMISS (9/29-10/1, Las Vegas, booths #107 & #109), NASS (10/12-10/14, Chicago, booth #5011), and EUROSPINE (10/19-10/21, Milan, #booth 102). For more information, visit https://www.pointroboticsinc.com
About Point Robotics MedTech Inc.
Point Robotics MedTech, a pioneer in minimally invasive surgical technologies, was founded in Taiwan in 2016. The Company has two major product lines: "Kinguide Robotic-Assisted Surgical System" and "Kinguide Agile Hybrid Navigation System. In May 2020, the company completed its series-A fundraising from government funds, Taiwania Capital, and Translink Capital. Point Robotics MedTech will continue to focus on the development and regulatory approvals of next-generation products to maintain its leading position in the high-end surgical robotics market.
View original content to download multimedia:https://www.prnewswire.com/news-releases/first-ever-spinal-surgical-hand-held-robot-by-point-robotics-medtech-to-make-its-worldwide-debut-in-the-united-states-301635498.html
SOURCE Point Robotics MedTech Inc.
Data analytics is key for reaching Net Zero
Climate change has created a new level of commitment from the automotive industry to reach net zero emissions.
This drive for sustainability has evolved from tailpipe emissions to manufacturing emissions to encompass the entire life cycle of the product.
The GHG Protocol establishes comprehensive global standardized frameworks to measure and manage greenhouse gas (GHG) emissions, which fall into three categories: scope 1, 2 and 3.
For automakers, scopes 1 and 2 are related to manufacturing a car (fuel consumption in production and assembly, electricity, heating, cooling, and steaming for operations).
Scope 3 involves upstream inputs such as purchased goods and services and logistics as well as the end-of-life treatment of the vehicle.
To make more sustainable products the starting point is no longer confined to just monitoring the production process. Companies must also enlarge their toolkits to allow them to modify product strategy and select environmentally aware supply chain partners.
For example, Ford has requested that all its Tier 1 suppliers disclose their carbon emissions. Ford used these numbers to calculate how much of the scope 3 emissions it was responsible for. The number was about 70 percent.
Moving beyond aggregated information
All this tells us that the path to Net Zero is a transformative process that must be guided with sufficient data insights to make the right strategic decisions.
Currently Ford, and other automakers, know their direct emissions, and some of them have a reasonably good estimate of the emissions that come from their value chains and how much comes from their facilities. However, it does not have the data to compare emissions from the F-150 pickup against the Explorer SUV because they sourced different parts, assembled them in different facilities, and they have different fuel efficiency. This is the vision of product-based emission accounting, which will be crucial to have to make effective business decisions.
With a product-based carbon footprint an automaker would know the carbon cost for every hour of welding, or for every painting of a surface space. This would allow them to understand the carbon footprint of every step in the production process. However, the current data tools companies use to collect and report emissions data are far not capable of providing this granular level of insight.
The data triangle
There are three parts of an effective sustainability data strategy -- first data sourcing and quality, secondly data management and consistency, and finally the analytics that is derived from the data.
The conundrum of data sourcing and quality is the first challenge that automakers are grappling with. Companies need data points that allow them to understand how much fuel they are spending on each part of the logistics chain.
There are numerous data sources, and they are fragmented across an organization. That’s why data ingestion tools that interface with emission activity data in different formats, structures, and even methods of ingestion are critical. They also reconcile different input data frequencies and reveal gaps and unusual values to maintain data quality.
Next up is the data management piece. Companies serious about measuring and tracking progress must develop a flexible, future-proof core database.
Without a centralized data model with a constant core set of data architecture and concepts it will be difficult to manage the ever more complex emission and climate related data. More importantly, the organizing structures of the data should remain flexible while the values and computations should remain consistent.
The third, and most important aspect is the ability to access that data and perform advanced analytics on it to deliver business insights that lead to achieving sustainability targets.
For example, having the ability to attribute different carbon footprint to different product lines and being able to to make decisions on how to prioritize or innovate the product portfolio.
This will enable an organization to be more selective about the environmental implications of its supply chain decisions. It can also shed light on the opportunities or limitations in adopting zero-carbon or low carbon energy sources for production and logistics.
Data analytics: Seeing through confusion
Beyond simply moving faster, data analytics simplifies and shortens distances to processing data and making better decisions.
“Data analytics isn’t difficult; it’s pretty easy and straightforward. It’s just that today we have a lot more data from a lot more sources, but what we do with it and the metrics we use are still the same,” says John Clemons, MES solutions consultant at Maverick Technologies, a system integrator and Rockwell Automation company. “Users still want to do everything better, faster and cheaper. They want to increase first-pass yields and productivity, reduce costs, and improve quality, output and margins. However, with more data and sources, they can begin to see interactions they couldn’t see before—even though it’s difficult to separate the wheat from the chaff.”
Brian Bolton, consultant specializing in Aveva PI applications at Maverick, reports that more information data and useful analytics gain added value as they’re made available across all departments in an organization. “For example, if the quality department can see production and finance data at the same time, they can determine what quality costs more precisely,” explains Bolton. “Or, if process data is combined with laboratory information management system (LIMS) software on quality, which weren’t combined electronically before, they can now be pulled into a historian and allow cross-platform reporting. This lets users slice and dice data by cost, and find facts like 20% of their products are contributing 80% of their profits, or learn which products aren’t profitable and adjust prices or stop selling them.”
Add value with analyticsBecause users can generate data about making their products closer to perfect, Bolton states they can also sell related information and services to add more value to existing products. This can include identifying break-even points or determining if an item should be sold as topline or midrange. “We work in the world of process historians, but now we can visualize everything,” adds Bolton. “The glory of HTML5 applications and software like Aveva’s PI Vision is that we can see information on any device, set up links to pull production and LIMS data, and perform analytics on them. This also lets us look at historian and live data together, so we can compare batches during every cycle to achieve greater consistency.”
Bolton acknowledges that many of these analytical functions were performed in the past, but users wielding clipboards, tracing paper and Excel spreadsheets typically didn’t have the time to mix and compare pieces of information coming from production, LIMS and other systems. “Fifteen years ago, we’d see a 200-300 centipoise range for a base resin precursor in plywood manufacturing, and if this enabled a 1,100-1,300 viscosity range, it would be no problem to make the product,” explains Bolton. “However, if viscosity could be maintained at 1,100, it would be easier to set up the equipment, and save material, time and adjustments. Now, we have historians and DCS software with data analytics that can let users know more about the attributes of their resin, and fully automate and monitor their batch process, so all they have to do is check a sample at the end.
“Veteran users worry that no one will know how to perform these tasks manually in the future, but data analytics can supplement what they know. By linking inline viscometers and pH meters to data analytics, we can see curves and trends immediately, know what’s happening as a batch is produced, maybe make adjustments on the fly, and prevent off-spec and bad batches before they occur.”
Skid to code to cloudFor instance, Clemons reports that one of Maverick’s clients is a skid builder that uses its controls, including a historian built into its onboard, edge-computing system. The historian connects to the user’s enterprise network and cloud-computing service twice a week to download all the data it’s collected. “This is a very simplified, plug-and-play system that was gradually developed, but it’s made each of their jobs a little easier,” says Clemons. “This builder previously used DOS and C++ software to gather and analyze data, but they don’t need it anymore. Now, instead of writing code from scratch, they can just grab the code they need from Github and import it, and add their own production numbers, which drastically reduces their programming time.”
Clemons adds the key to today’s data analytics is enabling LIMS and other systems to talk to whatever and whoever needs them. This includes allowing PI software to communicate via OPC UA and other protocols, and letting text files from machines be easily converted and added to applicable databases.
“If we can acquire and coordinate formerly disparate data, we can learn enough about what’s going on with processes to answer deeper ‘why’ questions about production, computerized maintenance management systems (CMMS) and materials from their suppliers. For example, if a final product is off spec, users can more easily identify problems with particular raw materials or cleaning and maintenance functions,” adds Clemons. “This is classic multivariate analysis, but it shows the value of bringing together data sources that weren’t together, and learning what we couldn’t know before. There isn’t just one dataset. There are different datasets for production, quality, suppliers, materials, maintenance and people. The intersection of these datasets is where we get the real insights that we couldn’t get until now.”
About the author: Jim MontagueJim Montague is executive editor of Control. He can be contacted at jmontague@endeavorb2b.com.
eWEEK TweetChat, October 18: Optimizing Your Data Analytics Practice
On Tuesday, Oct. 18, at 11 AM PT, @eWEEKNews will host its monthly #eWEEKChat. The topic will be Optimizing Your Data Analytics Practice, and it will be moderated by James Maguire, eWEEK’s Editor-in-Chief.
We’ll discuss – using Twitter – the challenges, awesome potential and best practices of data analytics. How can we manage and invest in data analytics now so that we can leverage its greatest future advantage?
How to Participate: On Twitter, use the hashtag #eWEEKChat to follow/participate in the discussion. But it’s easier and more efficient to use the real-time chat room link at CrowdChat.
Instructions are on the Optimizing Your Data Analytics Practice: Log in at the top right, use your Twitter handle to register. The chat begins promptly at 11 AM PT. The page will come alive at that time with the real-time discussion. You can join in or simply watch the discussion as it flows.
Special Guests: Optimizing Your Data Analytics PracticeThe list of experts for this month’s Tweetchat currently includes the following – please check back for additional expert guests:
Chat room real-time link: Go to the Crowdchat page. Sign in with your Twitter handle and use #eweekchat for the identifier.
Tweetchat Questions: Optimizing Data AnalyticsThe questions we’ll tweet about will include – check back for more/ revised questions:
What else is important about data analytics – what else should companies be aware of?
Go here for CrowdChat information.
#eWEEKchat Tentative Schedule for 2022*July 19: Getting Started with Low Code / No CodeAug 16: Overcoming Multicloud ChallengesSept 20: The Future of Edge ComputingOct 18: Optimizing Your Data Analytics PracticeNov 15: Building Your AI DeploymentDec 13: Enterprise Tech in 2023
*all topics subjects to change
James Maguire is eWeek's Editor-in-Chief and has been reporting on emerging technology for more than 15 years. He has won two ASBPE Awards of Excellence for in-depth feature articles about cloud computing and artificial intelligence. He has covered the gamut of enterprise and consumer technology, and regularly communicates with leading IT newsmakers, vendors and analysts.
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