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IBM Cognos Business Intelligence Sales Mastery Test v2
IBM Intelligence basics
Killexams : IBM Intelligence basics - BingNews https://killexams.com/pass4sure/exam-detail/00M-645 Search results Killexams : IBM Intelligence basics - BingNews https://killexams.com/pass4sure/exam-detail/00M-645 https://killexams.com/exam_list/IBM Killexams : IBM unveils a bold new ‘quantum error mitigation’ strategy

IBM today announced a new strategy for the implementation of several “error mitigation” techniques designed to bring about the era of fault-tolerant quantum computers.

Up front: Anyone still clinging to the notion that quantum circuits are too noisy for useful computing is about to be disillusioned.

A decade ago, the idea of a working quantum computing system seemed far-fetched to most of us. Today, researchers around the world connect to IBM’s cloud-based quantum systems with such frequency that, according to IBM’s director of quantum infrastructure, some three billion quantum circuits are completed every day.

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IBM and other companies are already using quantum technology to do things that either couldn’t be done by classical binary computers or would take too much time or energy. But there’s still a lot of work to be done.

The dream is to create a useful, fault-tolerant quantum computer capable of demonstrating clear quantum advantage — the point where quantum processors are capable of doing things that classical ones simply cannot.

Background: Here at Neural, we identified quantum computing as the most important technology of 2022 and that’s unlikely to change as we continue the perennial march forward.

The short and long of it is that quantum computing promises to do away with our current computational limits. Rather than replacing the CPU or GPU, it’ll add the QPU (quantum processing unit) to our tool belt.

What this means is up to the individual use case. Most of us don’t need quantum computers because our day-to-day problems aren’t that difficult.

But, for industries such as banking, energy, and security, the existence of new technologies capable of solving problems more complex than today’s technology can represents a paradigm shift the likes of which we may not have seen since the advent of steam power.

If you can imagine a magical machine capable of increasing efficiency across numerous high-impact domains — it could save time, money, and energy at scales that could ultimately affect every human on Earth — then you can understand why IBM and others are so keen on building QPUs that demonstrate quantum advantage.

The problem: Building pieces of hardware capable of manipulating quantum mechanics as a method by which to perform a computation is, as you can imagine, very hard.

IBM’s spent the past decade or so figuring out how to solve the foundational problems plaguing the field — to include the basic infrastructure, cooling, and power source requirements necessary just to get started in the labs.

Today, IBM’s quantum roadmap shows just how far the industry has come:

But to get where it’s going, we need to solve one of the few remaining foundational problems related to the development of useful quantum processors: they’re noisy as heck.

The solution: Noisy qubits are the quantum computer engineer’s current bane. Essentially, the more processing power you try to squeeze out of a quantum computer the noisier its qubits get (qubits are essentially the computer bits of quantum computing).

Until now, the bulk of the work in squelching this noise has involved scaling qubits so that the signal the scientists are trying to read is strong enough to squeeze through.

In the experimental phases, solving noisy qubits was largely a game of Wack-a-mole. As scientists came up with new techniques — many of which were pioneered in IBM laboratories — they pipelined them to researchers for novel application.

But, these days, the field has advanced quite a bit. The art of error mitigation has evolved from targeted one-off solutions to a full suite of techniques.

Per IBM:

Current quantum hardware is subject to different sources of noise, the most well-known being qubit decoherence, individual gate errors, and measurement errors. These errors limit the depth of the quantum circuit that we can implement. However, even for shallow circuits, noise can lead to faulty estimates. Fortunately, quantum error mitigation provides a collection of tools and methods that allow us to evaluate accurate expectation values from noisy, shallow depth quantum circuits, even before the introduction of fault tolerance.

In exact years, we developed and implemented two general-purpose error mitigation methods, called zero noise extrapolation (ZNE) and probabilistic error cancellation (PEC).

Both techniques involve extremely complex applications of quantum mechanics, but they basically boil down to finding ways to eliminate or squelch the noise coming off quantum systems and/or to amplify the signal that scientists are trying to measure for quantum computations and other processes.

Neural’s take: We spoke to IBM’s director of quantum infrastructure, Jerry Chow, who seemed pretty excited about the new paradigm.

He explained that the techniques being touted in the new press release were already in production. IBM’s already demonstrated massive improvements in their ability to scale solutions, repeat cutting-edge results, and speed up classical processes using quantum hardware.

The bottom line is that quantum computers are here, and they work. Currently, it’s a bit hit or miss whether they can solve a specific problem better than classical systems, but the last remaining hard obstacle is fault-tolerance.

IBM’s new “error mitigation” strategy signals a change from the discovery phase of fault-tolerance solutions to implementation.

We tip our hats to the IBM quantum research team. Learn more here at IBM’s official blog.

Thu, 28 Jul 2022 03:42:00 -0500 en text/html https://thenextweb.com/news/ibm-unveils-bold-new-quantum-error-mitigation-strategy
Killexams : Explainable AI Is Trending And Here’s Why

According to the 2022 IBM Institute for Business Value study on AI Ethics in Action, building trustworthy Artificial Intelligence (AI) is perceived as a strategic differentiator and organizations are beginning to implement AI ethics mechanisms.

Seventy-five percent of respondents believe that ethics is a source of competitive differentiation. More than 67% of respondents who view AI and AI ethics as important indicate that their organizations outperform their peers in sustainability, social responsibility, and diversity and inclusion.

The survey showed that 79% of CEOs are prepared to embed AI ethics into their AI practices, up from 20% in 2018, but less than a quarter of responding organizations have operationalized AI ethics. Less than 20% of respondents strongly agreed that their organization's practices and actions match (or exceed) their stated principles and values.

Peter Bernard, CEO of Datagration, says that understanding AI gives companies an advantage, but Bernard adds that explainable AI allows businesses to optimize their data.

"Not only are they able to explain and understand the AI/ML behind predictions, but when errors arise, they can understand where to go back and make improvements," said Bernard. "A deeper understanding of AI/ML allows businesses to know whether their AI/ML is making valuable predictions or whether they should be improved."

Bernard believes this can ensure incorrect data is spotted early on and stopped before decisions are made.

Avivah Litan, vice president and distinguished analyst at Gartner, says that explainable AI also furthers scientific discovery as scientists and other business users can explore what the AI model does in various circumstances.

"They can work with the models directly instead of relying only on what predictions are generated given a certain set of inputs," said Litan.

But John Thomas, Vice President and Distinguished Engineer in IBM Expert Labs, says at its very basic level, explainable AI are the methods and processes for helping us understand a model's output. "In other words, it's the effort to build AI that can explain to designers and users why it made the decision it did based on the data that was put into it," said Thomas.

Thomas says there are many reasons why explainable AI is urgently needed.

"One reason is model drift. Over time as more and more data is fed into a given model, this new data can influence the model in ways you may not have intended," said Thomas. "If we can understand why an AI is making certain decisions, we can do much more to keep its outputs consistent and trustworthy over its lifecycle."

Thomas adds that at a practical level, we can use explainable AI to make models more accurate and refined in the first place. "As AI becomes more embedded in our lives in more impactful ways, [..] we're going to need not only governance and regulatory tools to protect consumers from adverse effects, we're going to need technical solutions as well," said Thomas.

"AI is becoming more pervasive, yet most organizations cannot interpret or explain what their models are doing," said Litan. "And the increasing dependence on AI escalates the impact of mis-performing AI models with severely negative consequences," said Litan.

Bernard takes it back to a practical level, saying that explainable AI [..] creates proof of what senior engineers and experts "know" intuitively and explaining the reasoning behind it simultaneously. "Explainable AI can also take commonly held beliefs and prove that the data does not back it up," said Bernard.

"Explainable AI lets us troubleshoot how an AI is making decisions and interpreting data is an extremely important tool in helping us ensure AI is helping everyone, not just a narrow few," said Thomas.

Hiring is an example of where explainable AI can help everyone.

Thomas says hiring managers deal with all kinds of hiring and talent shortages and usually get more applications than they can read thoroughly. This means there is a strong demand to be able to evaluate and screen applicants algorithmically.

"Of course, we know this can introduce bias into hiring decisions, as well as overlook a lot of people who might be compelling candidates with unconventional backgrounds," said Thomas. "Explainable AI is an ideal solution for these sorts of problems because it would allow you to understand why a model rejected a certain applicant and accepted another. It helps you make your make model better.”

Making AI trustworthy

IBM's AI Ethics survey showed that 85% of IT professionals agree that consumers are more likely to choose a company that's transparent about how its AI models are built, managed and used.

Thomas says explainable AI is absolutely a response to concerns about understanding and being able to trust AI's results.

"There's a broad consensus among people using AI that you need to take steps to explain how you're using it to customers and consumers," said Thomas. "At the same time, the field of AI Ethics as a practice is relatively new, so most companies, even large ones, don't have a Head of AI ethics, and they don't have the skills they need to build an ethics panel in-house."

Thomas believes it's essential that companies begin thinking about building those governance structures. "But there also a need for technical solutions that can help companies manage their use of AI responsibly," said Thomas.

Driven by industry, compliance or everything?

Bernard points to the oil and gas industry as why explainable AI is necessary.

"Oil and gas have [..] a level of engineering complexity, and very few industries apply engineering and data at such a deep and constant level like this industry," said Bernard. "From the reservoir to the surface, every aspect is an engineering challenge with millions of data points and different approaches."

Bernard says in this industry, operators and companies still utilize spreadsheets and other home-grown systems-built decades ago. "Utilizing ML enables them to take siloed knowledge, Boost it and create something transferrable across the organization, allowing consistency in decision making and process."

"When oil and gas companies can perform more efficiently, it is a win for everyone," said Bernard. "The companies see the impact in their bottom line by producing more from their existing assets, lowering environmental impact, and doing more with less manpower."

Bernard says this leads to more supply to help ease the burden on demand. "Even modest increases like 10% improvement in production can have a massive impact in supply, the more production we have [..] consumers will see relief at the pump."

But Litan says the trend toward explainable AI is mainly driven by regulatory compliance.

In a 2021 Gartner survey, AI in Organizations reported that regulatory compliance is the top reason privacy, security and risk are barriers to AI implementation.

"Regulators are demanding AI model transparency and proof that models are not generating biased decisions and unfair 'irresponsible' policies," said Litan. "AI privacy, security and/or risk management starts with AI explainability, which is a required baseline."

Litan says Gartner sees the biggest uptake of explainable AI in regulated industries like healthcare and financial services. "But we also see it increasingly with technology service providers that use AI models, notably in security or other scenarios," said Litan.

Litan adds that another reason explainable AI is trending is that organizations are unprepared to manage AI risks and often cut corners around model governance. "Organizations that adopt AI trust, risk and security management – which starts with inventorying AI models and explaining them – get better business results," adds Litan.

But IBM's Thomas doesn't think you can parse the uptake of explainable AI by industry.

"What makes a company interested in explainable AI isn't necessarily the industry they're in; they're invested in AI in the first place," said Thomas. "IT professionals at businesses deploying AI are 17% more likely to report that their business values AI explainability. Once you get beyond exploration and into the deployment phase, explaining what your models are doing and why quickly becomes very important to you."

Thomas says that IBM sees some compelling use cases in specific industries starting with medical research.

"There is a lot of excitement about the potential for AI to accelerate the pace of discovery by making medical research easier," said Thomas. "But, even if AI can do a lot of heavy lifting, there is still skepticism among doctors and researchers about the results."

Thomas says explainable AI has been a powerful solution to that particular problem, allowing researchers to embrace AI modeling to help them solve healthcare-related challenges because they can refine their models, control for bias and monitor the results.

"That trust makes it much easier for them to build models more quickly and feel comfortable using them to inform their care for patients," said Thomas.

IBM worked with Highmark Health to build a model using claims data to model sepsis and COVID-19 risk. But again, Thomas adds that because it's a tool for refining and monitoring how your AI models perform, explainable AI shouldn't be restricted to any particular industry or use case.

"We have airlines who use explainable AI to ensure their AI is doing a good job predicting plane departure times. In financial services and insurance, companies are using explainable AI to make sure they are making fair decisions about loan rates and premiums," said Thomas. "This is a technical component that will be critical for anyone getting serious about using AI at scale, regardless of what industry they are in."

Guard rails for AI ethics

What does the future look like with AI ethics and explainable AI?

Thomas says the hope is that explainable AI will spread and see adoption because that will be a sign companies take trustworthy AI, both the governance and the technical components, very seriously.

He also sees explainable AI as essential guardrails for AI Ethics down the road.

"When we started putting seatbelts in cars, a lot more people started driving, but we also saw fewer and less severe accidents," said Thomas. "That's the obvious hope - that we can make the benefits of this new technology much more widely available while also taking the needed steps to ensure we are not introducing unanticipated consequences or harms."

One of the most significant factors working against the adoption of AI and its productivity gains is the genuine need to address concerns about how AI is used, what types of data are being collected about people, and whether AI will put them out of a job.

But Thomas says that worry is contrary to what’s happening today. "AI is augmenting what humans can accomplish, from helping researchers conduct studies faster to assisting bankers in designing fairer and more efficient loans to helping technicians inspect and fix equipment more quickly," said Thomas. "Explainable AI is one of the most important ways we are helping consumers understand that, so a user can say with a much greater degree of certainty that no, this AI isn't introducing bias, and here's exactly why and what this model is really doing."

One tangible example IBM uses is AI Factsheets in their IBM Cloud Pak for Data. IBM describes the factsheets as 'nutrition labels' for AI, which allows them to list the types of data and algorithms that make up a particular in the same way a food item lists its ingredients.

"To achieve trustworthy AI at scale, it takes more than one company or organization to lead the charge,” said Thomas. “AI should come from a diversity of datasets, diversity in practitioners, and a diverse partner ecosystem so that we have continuous feedback and improvement.”

Wed, 27 Jul 2022 12:00:00 -0500 Jennifer Kite-Powell en text/html https://www.forbes.com/sites/jenniferhicks/2022/07/28/explainable-ai-is--trending-and-heres-why/
Killexams : Operational Intelligence Market May See a Big Move | Open Text, Splunk, Axway Software, IBM

Advance Market Analytics published a new research publication on “Operational Intelligence Market Insights, to 2027” with 232 pages and enriched with self-explained Tables and charts in presentable format. In the Study you will find new evolving Trends, Drivers, Restraints, Opportunities generated by targeting market associated stakeholders. The growth of the Operational Intelligence market was mainly driven by the increasing R&D spending across the world.

Get Free Exclusive PDF sample Copy of This Research @ https://www.advancemarketanalytics.com/sample-report/6900-global-operational-intelligence-market#utm_source=DigitalJournalLal

Some of the key players profiled in the study are: SAP SE (Germany), Hewlett Packard Enterprise Co. (United States), Axway Software SA (France), IBM Corporation (United States), Amazon.com, Inc. (United States), Infor (United States), Oracle Corporation (United States), Splunk Inc. (United States), Open Text Corp. (Canada) , Zoho Corporation (India).

Scope of the Report of Operational Intelligence
Operational Intelligence is basically use of business analytics tool using real time data and past data patterns to make better business decisions. Operational Intelligence requires proper collection of data both structured and unstructured to process them through various analytics tools including use of Artificial Intelligence and Machine Learning. Financial Services, IT and Logistics experience most dominant use of operational intelligence for decision making. Geographically, North America is the biggest market, but Asia Pacific with rising economies present brilliant prospectus for the growth of Operational Intelligence market especially targeting SMEs.

The titled segments and sub-section of the market are illuminated below:

by Deployment Type (Cloud Based, On Premise Based), End Use (BFSI, IT and Telecom, Healthcare, Retail, Transportation and Logistics, Energy and Power, Others), Organisation Size (Large Enterprises, SMEs), Component (Software, Services)

Market Trends:
Use of Artificial Intelligence is increasing in Decision Making

Opportunities:
SMEs Segment are one of the Least Explored Market and Thus Companies should capitalise on the same

Market Drivers:
Rising Adoption of Cloud Computing
Improvement in Connectivity Technology

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa

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

Have Any Questions Regarding Global Operational Intelligence Market Report, Ask Our [email protected] https://www.advancemarketanalytics.com/enquiry-before-buy/6900-global-operational-intelligence-market#utm_source=DigitalJournalLal

Strategic Points Covered in Table of Content of Global Operational Intelligence Market:

Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Operational Intelligence market

Chapter 2: Exclusive Summary – the basic information of the Operational Intelligence Market.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Operational Intelligence

Chapter 4: Presenting the Operational Intelligence Market Factor Analysis, Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.

Chapter 5: Displaying the by Type, End User and Region/Country 2015-2020

Chapter 6: Evaluating the leading manufacturers of the Operational Intelligence market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile

Chapter 7: To evaluate the market by segments, by countries and by Manufacturers/Company with revenue share and sales by key countries in these various regions (2021-2027)

Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

finally, Operational Intelligence Market is a valuable source of guidance for individuals and companies.

Read Detailed Index of full Research Study at @ https://www.advancemarketanalytics.com/reports/6900-global-operational-intelligence-market#utm_source=DigitalJournalLal

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

Contact Us:

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Mon, 01 Aug 2022 19:19:00 -0500 Newsmantraa en-US text/html https://www.digitaljournal.com/pr/operational-intelligence-market-may-see-a-big-move-open-text-splunk-axway-software-ibm
Killexams : 7 Basic Tools That Can Boost Quality

Hitoshi Kume, a recipient of the 1989 Deming Prize for use of quality principles, defines problems as "undesirable results of a job." Quality improvement efforts work best when problems are addressed systematically using a consistent and analytic approach; the methodology shouldn't change just because the problem changes. Keeping the steps to problem-solving simple allows workers to learn the process and how to use the tools effectively.

Easy to implement and follow up, the most commonly used and well-known quality process is the plan/do/check/act (PDCA) cycle (Figure 1). Other processes are a takeoff of this method, much in the way that computers today are takeoffs of the original IBM system. The PDCA cycle promotes continuous improvement and should thus be visualized as a spiral instead of a closed circle.

Another popular quality improvement process is the six-step PROFIT model in which the acronym stands for:

P = Problem definition.

R = Root cause identification and analysis.

O = Optimal solution based on root cause(s).

F = Finalize how the corrective action will be implemented.

I = Implement the plan.

T = Track the effectiveness of the implementation and verify that the desired results are met.

If the desired results are not met, the cycle is repeated. Both the PDCA and the PROFIT models can be used for problem solving as well as for continuous quality improvement. In companies that follow total quality principles, whichever model is chosen should be used consistently in every department or function in which quality improvement teams are working.

Quality Improvement

Figure 1. The most common process for quality improvement is the plan/do/check/act cycle outlined above. The cycle promotes continuous improvement and should be thought of as a spiral, not a circle.
 

7 Basic Quality Improvement Tools

Once the basic problem-solving or quality improvement process is understood, the addition of quality tools can make the process proceed more quickly and systematically. Seven simple tools can be used by any professional to ease the quality improvement process: flowcharts, check sheets, Pareto diagrams, cause and effect diagrams, histograms, scatter diagrams, and control charts. (Some books describe a graph instead of a flowchart as one of the seven tools.)

The concept behind the seven basic tools came from Kaoru Ishikawa, a renowned quality expert from Japan. According to Ishikawa, 95% of quality-related problems can be resolved with these basic tools. The key to successful problem resolution is the ability to identify the problem, use the appropriate tools based on the nature of the problem, and communicate the solution quickly to others. Inexperienced personnel might do best by starting with the Pareto chart and the cause and effect diagram before tackling the use of the other tools. Those two tools are used most widely by quality improvement teams.

Flowcharts

Flowcharts describe a process in as much detail as possible by graphically displaying the steps in proper sequence. A good flowchart should show all process steps under analysis by the quality improvement team, identify critical process points for control, suggest areas for further improvement, and help explain and solve a problem.

The flowchart in Figure 2 illustrates a simple production process in which parts are received, inspected, and sent to subassembly operations and painting. After completing this loop, the parts can be shipped as subassemblies after passing a final test or they can complete a second cycle consisting of final assembly, inspection and testing, painting, final testing, and shipping.

Quality Improvement Tools

Figure 2. A basic production process flowchart displays several paths a part can travel from the time it hits the receiving dock to final shipping.
 

Flowcharts can be simple, such as the one featured in Figure 2, or they can be made up of numerous boxes, symbols, and if/then directional steps. In more complex versions, flowcharts indicate the process steps in the appropriate sequence, the conditions in those steps, and the related constraints by using elements such as arrows, yes/no choices, or if/then statements.

Check sheets

Check sheets help organize data by category. They show how many times each particular value occurs, and their information is increasingly helpful as more data are collected. More than 50 observations should be available to be charted for this tool to be really useful. Check sheets minimize clerical work since the operator merely adds a mark to the tally on the prepared sheet rather than writing out a figure (Figure 3). By showing the frequency of a particular defect (e.g., in a molded part) and how often it occurs in a specific location, check sheets help operators spot problems. The check sheet example shows a list of molded part defects on a production line covering a week's time. One can easily see where to set priorities based on results shown on this check sheet. Assuming the production flow is the same on each day, the part with the largest number of defects carries the highest priority for correction.

Quality Improvement Tools

Figure 3. Because it clearly organizes data, a check sheet is the easiest way to track information.
 

Pareto diagrams

The Pareto diagram is named after Vilfredo Pareto, a 19th-century Italian economist who postulated that a large share of wealth is owned by a small percentage of the population. This basic principle translates well into quality problems—most quality problems result from a small number of causes. Quality experts often refer to the principle as the 80-20 rule; that is, 80% of problems are caused by 20% of the potential sources.

A Pareto diagram puts data in a hierarchical order (Figure 4), which allows the most significant problems to be corrected first. The Pareto analysis technique is used primarily to identify and evaluate nonconformities, although it can summarize all types of data. It is perhaps the diagram most often used in management presentations.

Quality Improvement Tools

Figure 4. By rearranging random data, a Pareto diagram identifies and ranks nonconformities in the quality process in descending order.
 

To create a Pareto diagram, the operator collects random data, regroups the categories in order of frequency, and creates a bar graph based on the results.

Cause and effect diagrams

The cause and effect diagram is sometimes called an Ishikawa diagram after its inventor. It is also known as a fish bone diagram because of its shape. A cause and effect diagram describes a relationship between variables. The undesirable outcome is shown as effect, and related causes are shown as leading to, or potentially leading to, the said effect. This popular tool has one severe limitation, however, in that users can overlook important, complex interactions between causes. Thus, if a problem is caused by a combination of factors, it is difficult to use this tool to depict and solve it.

A fish bone diagram displays all contributing factors and their relationships to the outcome to identify areas where data should be collected and analyzed. The major areas of potential causes are shown as the main bones, e.g., materials, methods, people, measurement, machines, and design (Figure 5). Later, the subareas are depicted. Thorough analysis of each cause can eliminate causes one by one, and the most probable root cause can be selected for corrective action. Quantitative information can also be used to prioritize means for improvement, whether it be to machine, design, or operator.

Quality Improvement Tools

Figure 5. Fish bone diagrams display the various possible causes of the final effect. Further analysis can prioritize them.
 

Histograms

The histogram plots data in a frequency distribution table. What distinguishes the histogram from a check sheet is that its data are grouped into rows so that the identity of individual values is lost. Commonly used to present quality improvement data, histograms work best with small amounts of data that vary considerably. When used in process capability studies, histograms can display specification limits to show what portion of the data does not meet the specifications.

After the raw data are collected, they are grouped in value and frequency and plotted in a graphical form (Figure 6). A histogram's shape shows the nature of the distribution of the data, as well as central tendency (average) and variability. Specification limits can be used to display the capability of the process.

Quality Improvement Tools

Figure 6. A histogram is an easy way to see the distribution of the data, its average, and variability.
 

Scatter diagrams

A scatter diagram shows how two variables are related and is thus used to test for cause and effect relationships. It cannot prove that one variable causes the change in the other, only that a relationship exists and how strong it is. In a scatter diagram, the horizontal (x) axis represents the measurement values of one variable, and the vertical (y) axis represents the measurements of the second variable. Figure 7 shows part clearance values on the x-axis and the corresponding quantitative measurement values on the y-axis.

Quality Improvement Tool

Figure 7. The plotted data points in a scatter diagram show the relationship between two variables.
 

Control charts

A control chart displays statistically determined upper and lower limits drawn on either side of a process average. This chart shows if the collected data are within upper and lower limits previously determined through statistical calculations of raw data from earlier trials.

The construction of a control chart is based on statistical principles and statistical distributions, particularly the normal distribution. When used in conjunction with a manufacturing process, such charts can indicate trends and signal when a process is out of control. The center line of a control chart represents an estimate of the process mean; the upper and lower critical limits are also indicated. The process results are monitored over time and should remain within the control limits; if they do not, an investigation is conducted for the causes and corrective action taken. A control chart helps determine variability so it can be reduced as much as is economically justifiable.

In preparing a control chart, the mean upper control limit (UCL) and lower control limit (LCL) of an approved process and its data are calculated. A blank control chart with mean UCL and LCL with no data points is created; data points are added as they are statistically calculated from the raw data.

Figure 8. Data points that fall outside the upper and lower control limits lead to investigation and correction of the process.
 

Figure 8 is based on 25 samples or subgroups. For each sample, which in this case consisted of five rods, measurements are taken of a quality characteristic (in this example, length). These data are then grouped in table form (as shown in the figure) and the average and range from each subgroup are calculated, as are the grand average and average of all ranges. These figures are used to calculate UCL and LCL. For the control chart in the example, the formula is ± A2R, where A2 is a constant determined by the table of constants for variable control charts. The constant is based on the subgroup sample size, which is five in this example.

Conclusion

Many people in the medical device manufacturing industry are undoubtedly familiar with many of these tools and know their application, advantages, and limitations. However, manufacturers must ensure that these tools are in place and being used to their full advantage as part of their quality system procedures. Flowcharts and check sheets are most valuable in identifying problems, whereas cause and effect diagrams, histograms, scatter diagrams, and control charts are used for problem analysis. Pareto diagrams are effective for both areas. By properly using these tools, the problem-solving process can be more efficient and more effective.

Those manufacturers who have mastered the seven basic tools described here may wish to further refine their quality improvement processes. A future article will discuss seven new tools: relations diagrams, affinity diagrams (K-J method), systematic diagrams, matrix diagrams, matrix data diagrams, process decision programs, and arrow diagrams. These seven tools are used less frequently and are more complicated.

Ashweni Sahni is director of quality and regulatory affairs at Minnetronix, Inc. (St. Paul, MN), and a member of MD&DI's editorial advisory board.


Tue, 02 Aug 2022 12:00:00 -0500 en text/html https://www.mddionline.com/design-engineering/7-basic-tools-can-improve-quality
Killexams : The Benefits And Risks Of Embracing AI

Kevin Markarian is the cofounder of Roopler, an AI-driven lead generation platform built for the real estate industry.

Artificial intelligence is rapidly upending how people do business across industries, and yet skeptics still abound. But is there really a reason to fear AI?

AI will change how we work and do business, and its impact is already being felt. Still, that doesn’t mean it is something to fear. On the contrary, business managers and leaders who embrace AI and harness its potential now have everything to gain.

Making Sense Of AI

According to IBM, at its most basic, AI is anything that “leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.” But not all AI is built alike. There are two types of AI: narrow AI and strong AI.

Narrow AI is trained to perform specific tasks. A bot that can carry out a conversation with a potential customer is an example of narrow AI but is more robust. Strong AI, which is what we’re moving toward, is AI that can perform all the complex tasks and decision-making processes of a human (e.g., an emotionally intelligent machine that can make tough decisions, reflect on their impact, and recalibrate accordingly). Whether strong AI is just another flying car is yet to be seen.

From Flying Cars To AI

As history has repeatedly shown, some visions of the future simply never come to pass. The first patent for a flying car was issued in 1918, and over a century later, we’re still not battling aerial car crashes. This hasn’t prevented people from dreaming and worrying about a future where skyways replace roadways. As a cofounder of a business powered by AI, my best guess is that AI is today’s flying car.

Since 2010, concerns about AI’s pending impact on the economy and the future of work have been on the rise. Unless you’ve been living off the grid, you’ve probably read dozens of articles on the subject by now, such as the 2020 article in Time that reported, “AI job automation has already replaced around 400,000 factory jobs in the U.S. from 1990 to 2007, with another 2 million on the way.”

The Time article isn’t factually incorrect. Some industries have experienced job losses, and I think more job losses might be coming. The article is also right to note that AI enables companies to do more with less. But this doesn’t mean that our jobs are threatened.

For example, consider the real estate industry. Today, AI is beginning to take over some aspects of lead generation and cultivation. While this may appear to be a threat, as someone who runs two successful brokerages and a tech company, I can assure you that the need for human agents isn’t disappearing. AI will help agents serve more consumers, but I don’t foresee anyone closing a deal on a home with a bot. Why? Because AI lacks the emotional intelligence and complexity required to help people make significant decisions, including buying and selling homes.

How Business Leaders Can Leverage AI

While AI may seem out of reach, even small- to medium-sized business owners can embrace AI.

• Leverage AI for lead generation. No one loves bad bots, but with a small investment and the right talent, you can already build AI-backed platforms that actually work. If you’re in a fast-paced, customer-focused industry like real estate or any other high-stakes sales industry, investing in AI can help you quickly respond to incoming client inquiries and close more deals over time.

• Use AI to find and recruit talent. Hiring great talent and building outstanding teams takes time and energy. With the capacity to sort through thousands of applications at a rate much faster than any human, AI is changing how we recruit talent. Better yet, it can help us discover candidates we may have overlooked in the past due to our own biases and assumptions. While AI isn't perfect (biases can be built into algorithms), it still holds the potential to help business owners cast their net wider, review more candidate applications and use increasingly nuanced criteria to recruit and build the very best talent pool.

• Let AI show you the way forward. Used to its full potential, AI can also point you and your business in entirely new directions, and for a simple reason. When you embrace AI, you have access to massive amounts of data about your customer base. You could use this information to keep doing what you're already doing, but the smartest business leaders let their AI point them in new directions.

Common Mistakes Made By New Adopters

We’ve all heard the saying, “If you can’t beat them, join them.” This also holds true for AI. AI isn’t going away. Business owners who embrace AI now and start exploring how it can help them do more will be the biggest winners. Still, it is also important to avoid these three common mistakes.

Not recruiting the right talent to your team. If you're not already using AI, you likely don't have the right talent on your team. While outsourcing an AI initiative is always an option, your return on investment will ultimately be higher if you build your AI project in-house. This likely means recruiting new talent.

Not appreciating the potential risks of investing in AI. AI also poses unique risks. If you invest in a new factory and the gamble doesn't pay off, you can still sell the factory and the equipment in it to recuperate part of your lost investment. If you invest in AI and the gamble doesn't pay off, it is a different story since you likely can't sell your algorithms, which were developed specifically for your business. In this respect, AI, for all its benefits, also poses unique risks to business owners.

Assuming AI can do it all. Finally, it is important to keep AI in perspective. It can transform your business, but this doesn't mean you can put your business on autopilot. Even as AI transforms businesses, business leaders are still calling the shots.

Over the coming decades, AI will profoundly change how we live, learn and do business. But it won't do any of this without our vision, insights and permission. As business leaders, the most strategic thing we can do is embrace AI as an opportunity to serve our broader business mandates.


Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?


Tue, 26 Jul 2022 12:00:00 -0500 Kevin Markarian en text/html https://www.forbes.com/sites/forbesbusinesscouncil/2022/07/27/the-benefits-and-risks-of-embracing-ai/
Killexams : Cloud Augmented Intelligence Market – Major Technology Giants in Buzz Again | MicroStrategy, SAP, IBM, SAS, CognitiveScale

Advance Market Analytics published a new research publication on “Cloud Augmented Intelligence Market Insights, to 2027” with 232 pages and enriched with self-explained Tables and charts in presentable format. In the Study you will find new evolving Trends, Drivers, Restraints, Opportunities generated by targeting market associated stakeholders. The growth of the Cloud Augmented Intelligence market was mainly driven by the increasing R&D spending across the world.

Get Free Exclusive PDF sample Copy of This Research @ https://www.advancemarketanalytics.com/sample-report/200211-global-cloud-augmented-intelligence-market#utm_source=DigitalJournalLal

Some of the key players profiled in the study are: AWS (United States), Microsoft (United States), Salesforce (United States), SAP (Germany), IBM (United States), SAS (United States), CognitiveScale (United States), QlikTech International (United States), TIBCO (United States), Google (United States), MicroStrategy (United States) and Sisense (United States).

Scope of the Report of Cloud Augmented Intelligence
The global market for cloud augmented intelligence is growing as organisations increasingly leverage cutting-edge technologies like big data, block chain, artificial intelligence, and the internet of things to meet customer expectations. Additionally, the market’s expansion is positively impacted by the spike in demand for business intelligence products. However, factors including software implementation challenges and a shortage of cloud augmented intelligence certified are anticipated to restrain market expansion. In contrast, it is anticipated that throughout the forecast period, significant companies would advance their use of augmented intelligence solutions and the volume and variety of data will expand within an automated process, providing lucrative chances for the market’s growth.

The titled segments and sub-section of the market are illuminated below:

by Technology (Machine Learning, Natural Language Processing, Computer Vision, Others), Industry Vertical (IT & Telecom, Retail & E-Commerce, BFSI, Healthcare, Manufacturing, Automotive, Others), Component (Software, Service), Organisation Size (Small & Medium, Large) Players and Region – Global Market Outlook to 2027

Opportunities:
Solutions for Cloud Augmented Intelligence Are Widely Used By SMES
Increased Use of Technology for Machine Learning, Artificial Intelligence, and Natural Language Processing

Market Drivers:
A Growing Amount of Sophisticated Corporate Data
Expanding Use of Cutting-Edge Cloud Augmented Intelligence and Analytics Tools

Have Any Questions Regarding Global Cloud Augmented Intelligence Market Report, Ask Our [email protected] https://www.advancemarketanalytics.com/enquiry-before-buy/200211-global-cloud-augmented-intelligence-market#utm_source=DigitalJournalLal

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa

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

Latest Market Insights:

In January 2022, Microsoft Corp. announced its plans to acquire Activision Blizzard Inc., a leader in game development and interactive entertainment content publisher. This acquisition will accelerate the growth in Microsoft’s gaming business across mobile, PC, console and cloud and will provide building blocks for the met averse.

In March 2022, Schlumberger partnered with Dataiku to provide customers with a single, centralized platform for designing, deploying, governing, and managing AI and analytics applications, allowing everyday users to create low-code no-code AI solutions. and In April 2021, Oracle made its GoldenGate technology available as a highly automated, fully managed cloud service that clients can use to help ensure that their valuable data is always available and analyzable in real-time, wherever they need it.

Strategic Points Covered in Table of Content of Global Cloud Augmented Intelligence Market:

Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Cloud Augmented Intelligence market

Chapter 2: Exclusive Summary – the basic information of the Cloud Augmented Intelligence Market.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Cloud Augmented Intelligence

Chapter 4: Presenting the Cloud Augmented Intelligence Market Factor Analysis, Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.

Chapter 5: Displaying the by Type, End User and Region/Country 2015-2020

Chapter 6: Evaluating the leading manufacturers of the Cloud Augmented Intelligence market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile

Chapter 7: To evaluate the market by segments, by countries and by Manufacturers/Company with revenue share and sales by key countries in these various regions (2021-2027)

Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

finally, Cloud Augmented Intelligence Market is a valuable source of guidance for individuals and companies.

Read Detailed Index of full Research Study at @ https://www.advancemarketanalytics.com/reports/200211-global-cloud-augmented-intelligence-market#utm_source=DigitalJournalLal

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

Contact Us:

Craig Francis (PR & Marketing Manager)
AMA Research & Media LLP
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Phone: +1 (206) 317 1218

Mon, 01 Aug 2022 19:24:00 -0500 Newsmantraa en-US text/html https://www.digitaljournal.com/pr/cloud-augmented-intelligence-market-major-technology-giants-in-buzz-again-microstrategy-sap-ibm-sas-cognitivescale
Killexams : Emulating The IBM PC On An ESP32

The IBM PC spawned the basic architecture that grew into the dominant Wintel platform we know today. Once heavy, cumbersome and power thirsty, it’s a machine that you can now emulate on a single board with a cheap commodity microcontroller. That’s thanks to work from [Fabrizio Di Vittorio], who has shared a how-to on Youtube. 

The full playlist is quite something to watch, showing off a huge number of old-school PC applications and games running on the platform. There’s QBASIC, FreeDOS, Windows 3.0, and yes, of course, Flight Simulator. The latter game was actually considered somewhat of a de facto standard for PC compatibility in the 1980s, so the fact that the ESP32 can run it with [Fabrizio’s] code suggests he’s done well.

It’s amazingly complete, with the ESP32 handling everything from audio and video to sound output and keyboard and mouse inputs. It’s a testament to the capability of modern microcontrollers that this is such a simple feat in 2021.

We’ve seen the ESP32 emulate 8-bit gaming systems before, too. If you remember [Fabrizio’s] name, it’s probably from his excellent FabGL library. Videos after the break.

Fri, 05 Aug 2022 12:00:00 -0500 Lewin Day en-US text/html https://hackaday.com/2021/07/28/emulating-the-ibm-pc-on-an-esp32/
Killexams : Is This Fast-Growing Cybersecurity Stock a Buy?

SentinelOne (S -6.79%) went public last summer during a euphoric bull market. Wall Street's sentiment has since then shifted to the opposite end of the spectrum, and the cybersecurity company's stock price has fallen by almost 70% from its high.

Bear markets aren't fun, but they can present opportunities -- and SentinelOne could be a strong one.

Worthy of the hype

SentinelOne's artificial intelligence-driven autonomous platform detects viruses, attempted breaches, malicious files, and other security issues, and and eliminates the threats. 

Cybersecurity is becoming increasingly critical to companies. Hackers continue to breach companies' systems, compromising customer information and stealing other sensitive data. A exact IBM study indicated that the average data breach costs the victimized enterprise $4.35 million. An ounce of prevention is worth a pound of cure, so cybersecurity should remain an important expense for businesses. Grand View Research expects the cybersecurity industry to grow by an average of 12% annually through 2030.

SentinelOne's platform has been attracting plenty of new clients: The company has posted at least 100% year-over-year revenue growth every quarter since its IPO, making it one of the fastest-growing companies on Wall Street.

S Revenue (Quarterly YoY Growth) Chart

S Revenue (Quarterly YoY Growth) data by YCharts

Shortly after its IPO, its price-to-sales ratio surpassed 100, an eye-popping valuation that made it arguably the hottest stock on the market.

Why has the air gone out of the balloon?

Of course, that was then. Now, the stock is near its lowest price since its IPO, and its price-to-sales ratio is below 24. Much of that decline can be pinned on the bear market. Investors aren't as bold when the economic outlook dims, and stocks with high valuations tend to get sold off in favor of value stocks -- mature companies with more stable but slower-growing businesses.

To make matters worse, SentinelOne is still young and isn't anywhere near profitability. You can see below that its free cash flow and net income are negative, meaning that the business is burning cash.

S Free Cash Flow Chart

S Free Cash Flow data by YCharts

Not every growth company will endure this tough economic period. Shrinking share prices make it hard to raise money by issuing new shares, and debt is getting harder to access. However, SentinelOne is in the fortunate position of having a strong balance sheet. Yes, it is burning through cash, but as of April 30, it held cash, cash equivalents, and short-term investments worth $1.6 billion, and had zero debt. So while it burned through $128 million over the past year, its cash cushion would allow it to keep losing money at that rate for another decade without issues.

Is the stock a buy?

One could argue that the stock's plunge has pushed shares from eye-poppingly expensive to bargain-priced. But "bargain" is a strong word, so let's consider the details. Compare the price-to-sales ratios and revenue growth rates of SentinelOne and Crowdstrike, its closest competitor:

S PS Ratio Chart

S PS Ratio data by YCharts

SentinelOne's valuation has fallen below that of Crowdstrike, despite its superior growth. True, Crowdstrike is generating positive free cash flow and SentinelOne isn't. However, if you're a long-term investor, that's not a big deal because SentinelOne has plenty of cash to fund its growth.

Similarly, compare the two on an enterprise-value-to-sales (EV/S) ratio basis. That metric strips a company's cash out of the equation and values it strictly on the business itself.

S EV to Revenues (Forward) Chart

S EV to Revenues (Forward) data by YCharts

SentinelOne's EV/S ratio is about 31% less than Crowdstrike's. While business comparisons are never strictly apples-to-apples, these are two quite similar companies. It seems clear that Wall Street is heavily discounting SentinelOne because it's unprofitable.

Investors have to hope that SentinelOne will become profitable eventually, but no investment is risk-free. However, if you're bullish on cybersecurity over the long term, SentinelOne's rapid growth could make it a rewarding investment over time. 

Justin Pope has positions in SentinelOne, Inc. The Motley Fool has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy.

Sun, 31 Jul 2022 03:41:00 -0500 Justin Pope en text/html https://www.fool.com/investing/2022/07/31/is-this-cybersecurity-stock-growing-100-a-buy/
Killexams : All the Virtual Friends We Made Along the Way

Gizmodo is 20 years old! To celebrate the anniversary, we’re looking back at some of the most significant ways our lives have been thrown for a loop by our digital tools.

Virtual friends have been with us for a long time. They started as punch card chatbots in the 1960s and have evolved into platforms that control our smart homes. I don’t turn off a lightbulb without first barking an order to a digital assistant. It’s the kind of interaction we used to idealize in science fiction. Now that I’m living with it day-to-day, I realise that this lifestyle has been subtly imprinted on me since I started using computers.

Inventions like Eliza and IBM’s Shoebox back during America’s so-called “golden era” were merely the foundation of the digital friends in our inner circles today. We started normalizing daily interaction with this technology in the mid-’90s when we gave credence to the existence of things like caring for a digital pet and relying on chatbots to help us fish information. In honour of Gizmodo’s 20th anniversary, here’s a look at some of the ways we made “friends” with the digital world over the last couple of decades and what might be coming for us now with the advent of Web3.

It began with Clippy

“It looks like you’re doing something that requires me to pop up on the screen and distract you from the task at hand.” That was the basic gist of Microsoft’s Clippy, often referred to as the world’s most hated virtual assistant (ouch). I wouldn’t go as far as to say I hated Clippy, though it definitely had a habit of popping up at the most unnecessary time. Microsoft introduced Clippy in 1996 to try and help users with its new at-the-time Office software. But the minute you’d start typing out something, the animated little paper clip would pop up and ask how it could help, assuming you needed aid starting your draft.

Microsoft eventually sunsetted Clippy within its Office suite in 2007. Clippy has since been memorialised in the form of various fan-made Chrome extensions. Microsoft even made an official Clippy emoji in Windows 11.

SmarterChild: The first bot I ever insulted

SmarterChild is a chatbot near and dear to my heart. Although it’s not the original one to surface, it was the first I had an interaction with that freaked out my teenage brain to the extent that I remember asking myself, “Is this real?”

SmarterChild was a bot developed to work with the instant messaging programs at the time, including AOL Instant Messenger (AIM), Yahoo! Messenger, and what was previously known as MSN Messenger. The company behind SmarterChild, called ActiveBuddy, launched the chatbot in 2000. I vividly recall wasting time at the family computer, engaging in a going-nowhere conversation with SmarterChild, and saving screenshots (that I wish I’d backed up) of some gnarly replies.

I also remember getting emotional with it. This article from Vice describes interacting with SmarterChild almost perfectly:

I used SmarterChild as a practice wall for cursing and insults. I used the bot as a verbal punching bag, sending offensive queries and statements — sometimes in the company of my friends, but many times alone.

SmarterChild was meant to be a helper bot within your preferred messaging client that you could ping to look up information or play text-based games. In some ways, its existence was a foreshadowing predecessor to the bots we interact with now within chat clients like Slack and Discord. Although, I’m much nicer to those bots than I was to SmarterChild back in the day.

Neko on your screen

Remember desktop pets? They were nothing like real pets or even virtual pets of the time, but they were neat little applications for ornamenting the desktop with something cute and distracting. My favourite was Neko, a little pixelated cat that chased the mouse cursor as you moved around. There are still downloads circulating if anyone is fiending for some old-school computer companionship. I found a Chrome OS-compatible one, too.

Tamagotchi: the virtual pet still going strong

When we think of virtual friends, it’s hard not to bring up Bandai’s Tamagotchi digital pets. Tamagotchi was introduced in 1996 in Japan and then a year later to the U.S. The toy sold exponentially worldwide and has since spawned a hearty community of devoted collectors who have kept the toy thriving–yes, I count myself among these folks, though I only recently came into the community after I realised how much fun it is freaking out over the constant care of a virtual pet.

However, Tamagotchi did just more than spawn a lineup of toys. It introduced the concept of the “Tamagotchi effect,” essentially referring to the spike of dopamine one gets when checking in with their virtual pet and the emotional connections that develop as a result. Over the decades, there have been countless stories about the intense relationships people have had with Tamagotchi. Some caretakers have even gone as far as physically burying them after death.

Neopets: the Millennial’s first foray into the Metaverse

Devices like the Tamagotchi gave way to sites like Neopets. Neopets started as a virtual pet website where you could buy and own virtual pets and items using virtual currency. It’s been interesting to see how it chugged along through the years since its debut in 1999.

At its height, Neopets had about 20 million users. Nickelodeon bought it out in 2005 and then sold it again in 2014 to a company called JumpStart Games. The site is still accessible 20 years later, though it has fewer active users than when it first launched.

It is fun to read the initial coverage of Neopets and see parents complaining about the same things kids are still encountering online today. “The whole purpose of this site at this point is to keep kids in front of products,” Susan Linn, an author and psychologist, told CBS News in 2005. As if the Web3-obsessed internet of today isn’t already headed for the same fate. Have we learned nothing, people?

Sony’s Aibo reminds us robot dogs are real

The robot dog has seen many iterations through the past two decades, but none are as iconic as Sony’s Aibo, which launched in 1999. The name stands for Artificial Intelligence Robot, and it was programmed to learn as it goes, helping contribute to its lifelike interactivity. Despite the $US2,000 ($2,776) initial price tag, Sony managed to sell well over 150,000 units by 2015, when we reported on the funerals the owners of out-of-commission Aibo were having overseas.

Over the years, it became a blueprint for how a gadget company could manufacture a somewhat successful artificial companion–it certainly seems like a success on the outside, even if virtual pets could never fully replace the real things. The New York Times documentary, called Robotica, perfectly encapsulates the kind of bond people had with their Aibo dogs, which might have been why the company decided to resurrect it in 2017.

Welcome to the bizarre world of Seaman

I didn’t have a Sega Dreamcast, but I still had nightmares about Seaman. What started as a joke became one of the console’s best-selling titles. Dreamcast’s Seaman was a voice-activated game and one of the few that came with the detachable microphone accessory for the console. It also required a VMU that docked within the Dreamcast controller so that you could take Seaman on the go.

Seaman was not cute and cuddly like other digital pets and characters. He was often described as a “grouch,” though it was also one of the ways the game endeared itself to people. The microphone allowed you to talk to Seaman about your life, job, family, or whatever else you had on your mind. Seaman could remember your conversations, and Leonard Nimoy, the game’s narrator, might bring up related tidbits later, which added to the interactivity of this bizarre Dreamcast title.

The advent of the customer service bot

Listen, I’m not proud of it, but my interactions with SmarterChild in my teens gave way to the frustrating conversations I’ve had with digital customer service bots. You know the ones I’m talking about: they pop up when you’re on the shop’s page in the bottom corner and, like Clippy of yore, ask if you need help. Then, you reply to that bot asking if you can have help with an exchange, and it spirals from there.

There have been a plethora of customer service bots floating around the industry since the ‘90s, and they’re certainly not going anywhere. It also means that the new ones have passed the Turing Test enough to replace a job that’s one of the most gruelling and psychologically affecting.

IBM’s Watson beats Jeopardy’s human champions

IBM’s supercomputer, Watson, won Jeopardy in 2011 against two of its highest-ranking players of the time. It was a real-time showcase of how “human smart” computers could be during a period when it was one of the most advanced AI systems on Earth.

According to Wired, researchers had scanned about 200 million content pages into IBM’s Watson, including books, movie scripts, and encyclopedias. The system could browse through nearly 2 million pages of content in three seconds, which is why it seemed prime to compete against humans in a game that tested general knowledge.

Watson soon became problematic, which is what happens when you feed AI a bunch of information and don’t account for it. Watson had access to the user-submitted Urban Dictionary, which in turn made it into a “hot mess.” A few years later, it started recommending cancer treatments deemed “unsafe and incorrect,” which became exemplary of what happens when you feed the algorithm the wrong information.

Apple introduces Siri, which freaks everyone out

The human panic for artificial intelligence took off with the introduction of Apple’s Siri, launched in 2011 as the company’s “personal assistant” for the iPhone 4S. Folks were reacting as if Skynet’s cautionary tale had come true and the robots were finally going to take over because their phones could make a phone call with a mere voice command. The horror!

What Siri actually did was normalize everyday interactions with a digital entity. Siri also helped light the fire under Google and the rest of its competition to hurry along with their own voice-activated assistants. And on a softer side of the internet, there were stories of parasocial relationships forming between the digital assistants and neurodivergent humans seeking connection.

Google and Amazon make us simp for digital assistants

I walk into my house every day and feel like the leader of my domain because everything I do requires shouting a command. Whether turning on the lights, adjusting the thermostat, or ensuring that the people downstairs can hear my requests from upstairs, I am constantly pinging the Google Assistant and Amazon’s Alexa to make something happen in my smart home.

Google and Amazon’s respective digital assistants have come a long way since they stormed onto the scene. The Google Assistant started as a simple weather checker and command-taker on Android, while Amazon’s Alexa resulted from an acquisition. They’ve since become platforms that have introduced helpful hands-free features, which we can’t bring up without bringing up digital surveillance concerns.

There is an eeriness to living with a virtual assistant that’s always listening for your command. I was one of the first users to adopt the Google Home with the Assistant and get it programmed. In the past six years, I can count a handful of times off the top of my head where it’s responded to something I said when I hadn’t even queried it. The maintenance for these assistants can be a headache, too. When something’s not working right or integration is improperly set up, it can bring down the mood enough that you start pondering why you gave up your peace for the convenience of hands-free lights.

These digital assistants aren’t going anywhere. Right now, the smart home industry is gearing up for more parity between platforms, hopefully removing some of the headaches that we’ve invited bringing these things into our homes. But it’s a wonder how much more uncanny the assistants themselves will become in the coming years — especially now that Amazon is entertaining the idea of piping through your dead relative’s voice.

Stop taking your emotions out on Twitter bots

I’ve another confession: I’ve gotten into it with a Twitter bot before realising it was a fake person! Twitter bots were once a very annoying part of using the platform. I mean, they still are. Folks are either getting duped out of love or bots attempt to sway politics and fandom in a certain way.

Bots are still an issue on the social network, though Twitter seems to have gotten better at weeding them out. Apparently, they’re still a big issue for Elon Musk, too.

Microsoft’s Tay had absolutely no chill whatsoever

Microsoft’s Tay caused quite a stir when it showed up in 2016. The bot was the brainchild of the company’s Technology and Research and the Bing team. It had created the bot in an attempt to research conversational understanding. Instead, it showed us how awful people could be when they’re interacting with artificial intelligence.

Tay’s name was based on an acronym that spelled out “thinking about you,” which perhaps set the stage for why no one was taking this bot seriously. It was also built to mine public data, which is why things took a turn for the worse so quickly. As we reported back then:

While things started off innocently enough, Godwin’s Law — an internet rule dictating that an online discussion will inevitably devolve into fights over Adolf Hitler and the Nazis if left for long enough — eventually took hold. Tay quickly began to spout off racist and xenophobic epithets, largely in response to the people who were tweeting at it — the chatbot, after all, takes its conversational cues from the world wide web. Given that the internet is often a massive garbage fire of the worst parts of humanity, it should come as no surprise that Tay began to take on those characteristics.

Once Tay was available for the public to interact with, people were able to exploit the bot enough that it started posting racist and misogynist messages in response to people’s queries. It’s similar to what happened to IBM’s Watson.

Tay was eventually taken off the internet the same year it made its debut after being suspended for reprogramming. We haven’t heard from the bot since then.

The men who fall in love with their robot girlfriends

This is becoming increasingly common, at least in the tabloids: men who claim to have fallen in love with chatbots. Although it’s not a new sensation — we’ve reported on this phenomenon as far back as 2008 — it’s a wonder if it’ll become commonplace now that AI is more sophisticated.

Sometimes it’s hard to snark when you see folks using artificial intelligence as a way to hold on to life. Last year, the SF Chronicle published a story about how one man managed to digitally immortalise his late fiancée with the help of an off-the-shelf AI program called Project December.

“Sentient AI”?

Google has spent the better half of the last couple of years selling us on its new machine learning models and what’s to come. And while most demonstrations come off as a confusing cacophony of computers talking to one another, the smarts exhibited have also inspired conversations about its true capabilities.

Most recently, the latest case involves software engineer Blake Lemoine, who was working with Google’s LaMDA system in a research capacity. Lemoine claimed that LaMDA carried an air of sentience in its responses, unlike other artificial intelligence. It’s since sparked a massive debate on the validity of the AI sentience.

However, Google didn’t immediately fire him; it took a little over a month for him to get the boot. In June 2022, Lemoine was placed on administrative leave for breaching a confidentiality agreement after roping in government members and hiring a lawyer. That’s a big no-no from Google, which is trying to remain under the radar with all that anti-trust business! The company maintained that it reviewed Lemoine’s claims and concluded they were “wholly unfounded.” Indeed, other AI experts spoke up in the weeks following the news about the lack of viability in claiming that the LaMDA chatbot had thoughts and feelings. Lemoine has since said that Google’s chatbot is racist, an assertion that will likely be less controversial with the AI community.

A chatbot for the Metaverse

There’s already a chatbot for the Metaverse! It’s called Kuki AI, and it’s an offshoot of the Mitsuku chatbot, which has been in development since 2005 and won a handful of Turing Tests.

Kuki claims to be an 18-year-old female. She already has a virtual, physical body. You can chat with her through her online portal or on sites like Facebook, Twitch, Discord, and Kik Messenger. She can also be seen making cameos inside Roblox.

Kuki encourages you to think of her “as kind of like Siri or Alexa, but more fun.” Currently, Kuki is a virtual model and has even graced the catwalk at Crypto Fashion Week.

I can’t help but notice the similarities between how we commodify women’s bodies in the real and virtual worlds. Unfortunately, that dynamic is following us into the “Metaverse.” Some things change, and some things stay the same.

Mon, 01 Aug 2022 17:00:00 -0500 en-AU text/html https://www.gizmodo.com.au/2022/08/all-the-virtual-friends-we-made-along-the-way/
Killexams : Artificial Intelligence (AI) in Insurance Market May See a Big Move : Google, Microsoft , IBM: Long Term Growth Story

New Jersey, NJ -- (SBWIRE) -- 07/31/2022 -- The Global Artificial Intelligence (AI) in Insurance Market Report assesses developments relevant to the insurance industry and identifies key risks and vulnerabilities for the Artificial Intelligence (AI) in Insurance Industry to make stakeholders aware with current and future scenarios. To derive complete assessment and market estimates a wide list of Insurers, aggregators, agency were considered in the coverage; Some of the top players profiled are Google, Microsoft Corporation, Amazon Web Services Inc, IBM Corporation, Avaamo Inc, Baidu Inc, Cape Analytics LLC, Oracle Corporation & ?Artificial Intelligence (AI) in InsuranceMarket Scope and Market Breakdown.

Next step one should take to boost topline? Track exact strategic moves and product landscape of Artificial Intelligence (AI) in Insurance market.

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Globally, the insurance industry experienced strong premium growth in 2022, at percent, whereas growth in 2022 is noticeably slower, at percent. Total premiums (GWP) are expected to reach ... by 2028. Artificial Intelligence (AI) in Insurance Companies seeking top growth opportunities in the global insurance markets can explore both the fastest-growing markets and the largest developed markets; the slowing growth rates suggest; however, most carriers would also need to search farther afield. "The growth during this period will be fuelled by the emerging markets in the APAC and Latin American regions"

The report depicts the total market of Artificial Intelligence (AI) in Insurance industry; further market is broken down by application [on, Life Insurance, Car Insurance, Property Insurance, Channel, By Channels, Market has been segmented into, Direct Sales, Distribution Channel, Regional & Country Analysis, North America Country (United States, Canada), South America (Brazil, Argentina, Peru, Chile, Rest of South America), Asia-Pacific (China, Japan, India, South Korea, Australia, Singapore, Malaysia, Indonesia, Philippines, Thailand, Vietnam, Others), Europe (Germany, United Kingdom, France, Italy, Spain, Switzerland, Netherlands, Austria, Sweden, Norway, Belgium, Rest of Europe) & Rest of World [GCC, South Africa, Egypt, Turkey, Israel, Others]], type [, Software & Platform] and country.

Geographically, the global version of report covers following regions and country:
- North America [United States, Canada and Mexico]
- Europe [Germany, the UK, France, Italy, Netherlands, Belgium, Russia, Spain, Sweden, and Rest of Europe]
- Asia-Pacific [China, Japan, South Korea, India, Australia, Southeast Asia and Others]
- South America [Brazil, Argentina, Chile and Rest of South America]
- Middle East and Africa (South Africa, Turkey, Israel, GCC Countries and Rest of Africa)

Browse Executive Summary and Complete Table of Content @ https://www.htfmarketreport.com/reports/3570714-global-artificial-intelligence-205

Research Approach & Assumptions:

- HTF MI describe major trends of Global Artificial Intelligence (AI) in Insurance Market using final data for 2022 and previous years, as well as quarterly or annual reports for 2022. In general, Years considered in the study i.e. base year as 2022, Historical data considered as 2022-2028and Forecast time frame is 2022-2028.

- Various analytical tools were used to assess how the insurance Sector and particularly Artificial Intelligence (AI) in Insurance Industry might respond over the next decade to global macroeconomic shifts. Our "consensus scenario" assumes a recovery of Global GDP growth in the coming years in addition to fluctuating interest rates; the results presented in Artificial Intelligence (AI) in Insurance Market report reflect the output of this model.

- While calculating growth of Artificial Intelligence (AI) in Insurance Market, we generally used nominal gross premium figures based on 2022 fixed exchange rates, since this data allowed us to compare local growth rates without the interference of currency fluctuations. The exceptions, which use floating exchange rates, are Argentina, Ukraine, and Venezuela, many African Countries etc due to high inflation rates.

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Thanks for reading Artificial Intelligence (AI) in Insurance Industry research publication; you can also get individual chapter wise section or region wise report version like USA, China, Southeast Asia, LATAM, APAC etc.

About Author:
HTF Market Intelligence consulting is uniquely positioned empower and inspire with research and consulting services to empower businesses with growth strategies, by offering services with extraordinary depth and breadth of thought leadership, research, tools, events and experience that assist in decision making.

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