#Process Analytics

[oliviadlima]

Process Analytics Market : Global Opportunity Analysis and Industry Forecast, 2021-2031

The global process analytics market size was valued at $469.86 million in 2021, and is projected to reach $18.6 billion by 2031, growing at a CAGR of 44.6% from 2022 to 2031.

Read More: https://www.alliedmarketresearch.com/process-analytics-market

Download Sample Report: https://www.alliedmarketresearch.com/request-sample/5970

[bogos-bint3d]

ok ok I trust you to be as insane about this as I am

in the seven eight nine dialogue, when Alphys talks about how seven loved five and was just doing what they felt they had to, Toriel interprets it as being Asgore doing what he had to for monsterkind, but I think she was actually thinking of Undyne doing what she felt like she had to by attacking Frisk, because she loves Asgore (and Alphys). being "sicced" on them as the captain of the royal guard, and playing the rule of the "misled antihero" in undertale's story.

when Toriel says that this makes seven "weak", Undyne bursts in with a distracting display of strength, trying to dismiss the topic. you could make the argument that she just wants to defuse Toriel from being mad at Asgore, but she has a seriously pissed look on her face. as if she took that personally, but also didn't know how to argue with it...

ANON. ANON YOU ARE SO FUCKING CORRECT FOR THIS HOLY SHIT. ANON. ANON WHO ARE YOU ILY

DUDE YOUR MIND UXJSJSJSJSJSJSJJSJD IM AO CTAZY ABOUT THIS MAN TY FOR SENDKNG THIS YOU ARE SO SMART OML SHSHSJSKSKSKK

^actual image of me right now thank you so so so so much anonymous. When I find you.

[emeraldmew]

Chapman!

Quite a different Chapman than the man we see in The Visitor. Like Elfangor there's a question of how Hendrick gets from point A to point B brought into play from the moment he appears.

Anyway, who doesn't want to examine the journey from terrible teenager to tragic father figure who sacrifices everything for his daughter?

[theroadtosomewhere]

was thinking in the shower today abt the rise of gen ai in education amongst students with the cheating + no original assignments etc and it really is a symptom of the fast-paced, quick-fix dopamine-dependent society that we’re in. Doubled down with the fact that so much of what’s taught at school (maybe this is just a western thing, likely) just isn’t relevant, the wrong history is taught and the system wasn’t built to benefit everyone to begin with, but an indoctrination into understanding how long a workday was meant to be. kids see education as a barrier to getting what they want now instead of it being the literal process because now there’s something else that can do it for them. problem is how can a child without prior introduction discern what they actually need to take from their education.

anyway fuck generative ai. suck my dick i got flagged false positive at uni and sat in a room where two people told me that i wrote like ai and that it should be a compliment and not something I’m gonna have to constantly deal with. let’s go there with the argument about how NDs are likely disproportionately affected.

[thanatika]

I’ve seen you refer to it in usually a negative way in your posts sometimes but what is your personal stance on Hbomb’s patholgic video? Do you have any thoughts on the other popular pathologic videos or are they nothing of note positive or negative

haha, thanks for the ask. you're right, i probably do sound like i'm hating on it most of the time that i mention it.

but to be honest, i heard about pathologic for the first time by watching the hbomberguy video, and i'm not sure if i ever would have encountered the game if he hadn't made it! and i think it's a really interesting, well-made video.

i honestly think that my problems with the video have more to do with the fact that i got a little obsessed with the game and ended up knowing more about it than he does, rather than the video being inherently bad. it has a lot of inaccuracies, but if you take it as a subjective work that follows his personal experience playing the game, i'm okay with it.

(and i think this is something that can be applied to most of his videos, especially about media: i've realized that he often overemphasizes or downplays things to make a certain point. i remember thinking this about his fallout new vegas video, that he was making the game sound way more incredible than it actually is, speaking as a FNV fan myself. or his RWBY video, which i watched despite having never seen the show, and then encountered a RWBY fan in the wild who insisted that the video misrepresented the show. but again, i think this isn't necessarily a problem-- he's giving his own take, and trying to be entertaining.)

honestly, my biggest problem with the video is the end, where he basically tells people that they should just skip pathologic classic and go straight to P2, as if the two are interchangeable experiences and P2 is just the updated version. even if the other two routes had been added to P2 in a reasonable amount of time, which it seems like hbomb was expecting back when he made the video, artemy's story in P2 and classic are so different. they aren't interchangeable.

aside from that, i'm more frustrated by the impact that i feel the video had on the fanbase than anything (people not trying classic because he scared them off of it, fans who only watch his and other videos about the games without even trying to watch a lets play or something, people basing their preconceived notions about the characters on his jokey descriptions of them).

[okaytosave]

I love the community here, for so many of you are posting analysis and reflections that have me even more intrigued than I thought I already was.

Meanwhile I’m sitting here wondering if Lestat and Louis, Louis and Claudia, Louis and Armand, ever held a wine tasting together but more commentary was on blood type and that sort of refinery.

[37-feral-raccoons]

comp sci majors who also hate generative AI reblog please I need to know some people in my field are sane 😭

[gemharvest]

(notices all the mannerisms from my friends slip into the way i talk) (forgets this is normal human behavior) god i hope nobody notices i'm just a mimicry of them and not my own real person.

[raedas]

the thing about being aro is after a while you legitimately start to go hey i don’t think romance is real actually

[oliviadlima]

Process Analytics Market - An Emerging Hint of Opportunity

According to a new report published by Allied Market Research, titled, “Process Analytics Market, by Process Mining Type (Process discovery, Process conformance, Process enhancement), by Deployment Mode (On Premise, Cloud), by Organization Size (Large Enterprises, Small and Medium Enterprises), by Application (Business Process, Information Technology Process, Customer Interaction): Global Opportunity Analysis and Industry Forecast, 2021–2031”.

The process analytics market was valued at $469.86 million in 2021, and is estimated to reach $18.6 billion by 2031, growing at a CAGR of 44.6% from 2022 to 2031.

Process analytics is a type of system that helps in analyzing and controlling the manufacturing processes. It also measures critical quality parameters and performance attributes of raw and in-process materials. In addition, it enables in-process data to be used for assessing the quality of a batch during manufacture, significantly reduces the need for finished product testing, and as a result, improves lead times. Furthermore, digital transformation is empowering users’ awareness for analyzing and understanding business processes and increase in adoption of algorithmic business are the significant growth opportunities for the future prospects of process analytics market forecast. In addition, rise in implementation of task-level automation is positively impacting the growth of the process analytics market. However, competition from open-source alternatives is hampering the demand for commercial solutions and lack of skills and expertise limit the market growth. On the contrary, the need to focus on enhancing the customer experience is expected to offer remunerative opportunities for expansion of the process analytics industry during the forecast period.

On the basis of organization size, the large enterprises segment dominated the overall process analytics market in 2021, and is expected to continue this trend during the forecast period. This is attributed to process analytics solutions enabling enterprises to monitor and regulate complex business and Information Technology (IT) processes. However, small and medium size enterprise segment is expected to witness highest growth as various SMEs industries from different domains have been trying to build process system infrastructures that are more dynamic, agile, and cost-efficient, which is expected to further fuel the growth of the global process analytics industry.

Region wise, the process analytics market was dominated by North America in 2021, and is expected to retain its position during the forecast period, owing to rapidly growing digital landscape of the region and rise in initial adoption of advanced process analytics technology by the manufacturing enterprises. However, owing to its growing adoption of process analytics in manufacturing and IT & telecom sector across the developing countries in this region, is anticipated to drive the industry growth for the processing analytics market in Asia-Pacific region.

Inquiry Before Buying: https://www.alliedmarketresearch.com/purchase-enquiry/5970

The spread of COVID-19 has been disrupting the world, businesses, and economies and has impacted the way of living of the masses and approaches adopted by enterprises for business management. The ability of enterprises to sustain this pandemic has become challenging for them as they are shifting focus from growth opportunities and concentrate on implementing measures to mitigate the impact of COVID-19. The upcoming analytics projects are kept on hold owing to the pandemic. Several companies are competing with each other to gain a process analytical solution to adopt the process analytics. Businesses have already started efforts to return to their fully operating conditions and are facing multiple challenges in terms of enhancing the process, due to impact of the pandemic on customer base and operations. Therefore, companies are looking forward to update their manufacturing process by implementing process analytics.

KEY FINDINGS OF THE STUDY

By process mining type, the process discovery segment accounted for the largest process analytics market share in 2021.

By region, North America generated highest revenue in 2021.

By organization size, the large enterprise segment generated the highest revenue in 2021.

The key players profiled in the process analytics market analysis are Celonis, Fluxicon BV, Icaro Tech, Kofax Inc., Logpickr, Scheer GmbH, Worksoft, Inc., Minit, Monkey Mining BV, and ABBYY. These players have adopted various strategies to increase their market penetration and strengthen their position in the industry.

About Us:

Allied Market Research (AMR) is a full-service market research and business-consulting wing of Allied Analytics LLP based in Portland, Oregon. Allied Market Research provides global enterprises as well as medium and small businesses with unmatched quality of “Market Research Reports Insights” and “Business Intelligence Solutions.” AMR has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.

[eldritch-elrics]

speaking of dark souls yaoi. i'm thinking again about that genre of posts that basically implies that shipping precludes thoughtful analysis of a piece of media.. and those always annoy me because i GET it. i have read fanfics/posts that have made me think the exact same thing. at the same time..... my favorite intellectual exercise is "how much thoughtful character analysis & thematic resonance can i fit in this gay sex fanfiction" so sometimes it feels like the authors of those sorts of posts have not expanded their minds to the possibilities of what can be achieved through gay sex

[innovatexblog]

How Large Language Models (LLMs) are Transforming Data Cleaning in 2024

Data is the new oil, and just like crude oil, it needs refining before it can be utilized effectively. Data cleaning, a crucial part of data preprocessing, is one of the most time-consuming and tedious tasks in data analytics. With the advent of Artificial Intelligence, particularly Large Language Models (LLMs), the landscape of data cleaning has started to shift dramatically. This blog delves into how LLMs are revolutionizing data cleaning in 2024 and what this means for businesses and data scientists.

The Growing Importance of Data Cleaning

Data cleaning involves identifying and rectifying errors, missing values, outliers, duplicates, and inconsistencies within datasets to ensure that data is accurate and usable. This step can take up to 80% of a data scientist's time. Inaccurate data can lead to flawed analysis, costing businesses both time and money. Hence, automating the data cleaning process without compromising data quality is essential. This is where LLMs come into play.

What are Large Language Models (LLMs)?

LLMs, like OpenAI's GPT-4 and Google's BERT, are deep learning models that have been trained on vast amounts of text data. These models are capable of understanding and generating human-like text, answering complex queries, and even writing code. With millions (sometimes billions) of parameters, LLMs can capture context, semantics, and nuances from data, making them ideal candidates for tasks beyond text generation—such as data cleaning.

To see how LLMs are also transforming other domains, like Business Intelligence (BI) and Analytics, check out our blog How LLMs are Transforming Business Intelligence (BI) and Analytics.

Traditional Data Cleaning Methods vs. LLM-Driven Approaches

Traditionally, data cleaning has relied heavily on rule-based systems and manual intervention. Common methods include:

Handling missing values: Methods like mean imputation or simply removing rows with missing data are used.

Detecting outliers: Outliers are identified using statistical methods, such as standard deviation or the Interquartile Range (IQR).

Deduplication: Exact or fuzzy matching algorithms identify and remove duplicates in datasets.

However, these traditional approaches come with significant limitations. For instance, rule-based systems often fail when dealing with unstructured data or context-specific errors. They also require constant updates to account for new data patterns.

LLM-driven approaches offer a more dynamic, context-aware solution to these problems.

How LLMs are Transforming Data Cleaning

1. Understanding Contextual Data Anomalies

LLMs excel in natural language understanding, which allows them to detect context-specific anomalies that rule-based systems might overlook. For example, an LLM can be trained to recognize that “N/A” in a field might mean "Not Available" in some contexts and "Not Applicable" in others. This contextual awareness ensures that data anomalies are corrected more accurately.

2. Data Imputation Using Natural Language Understanding

Missing data is one of the most common issues in data cleaning. LLMs, thanks to their vast training on text data, can fill in missing data points intelligently. For example, if a dataset contains customer reviews with missing ratings, an LLM could predict the likely rating based on the review's sentiment and content.

A recent study conducted by researchers at MIT (2023) demonstrated that LLMs could improve imputation accuracy by up to 30% compared to traditional statistical methods. These models were trained to understand patterns in missing data and generate contextually accurate predictions, which proved to be especially useful in cases where human oversight was traditionally required.

3. Automating Deduplication and Data Normalization

LLMs can handle text-based duplication much more effectively than traditional fuzzy matching algorithms. Since these models understand the nuances of language, they can identify duplicate entries even when the text is not an exact match. For example, consider two entries: "Apple Inc." and "Apple Incorporated." Traditional algorithms might not catch this as a duplicate, but an LLM can easily detect that both refer to the same entity.

Similarly, data normalization—ensuring that data is formatted uniformly across a dataset—can be automated with LLMs. These models can normalize everything from addresses to company names based on their understanding of common patterns and formats.

4. Handling Unstructured Data

One of the greatest strengths of LLMs is their ability to work with unstructured data, which is often neglected in traditional data cleaning processes. While rule-based systems struggle to clean unstructured text, such as customer feedback or social media comments, LLMs excel in this domain. For instance, they can classify, summarize, and extract insights from large volumes of unstructured text, converting it into a more analyzable format.

For businesses dealing with social media data, LLMs can be used to clean and organize comments by detecting sentiment, identifying spam or irrelevant information, and removing outliers from the dataset. This is an area where LLMs offer significant advantages over traditional data cleaning methods.

For those interested in leveraging both LLMs and DevOps for data cleaning, see our blog Leveraging LLMs and DevOps for Effective Data Cleaning: A Modern Approach.

Real-World Applications

1. Healthcare Sector

Data quality in healthcare is critical for effective treatment, patient safety, and research. LLMs have proven useful in cleaning messy medical data such as patient records, diagnostic reports, and treatment plans. For example, the use of LLMs has enabled hospitals to automate the cleaning of Electronic Health Records (EHRs) by understanding the medical context of missing or inconsistent information.

2. Financial Services

Financial institutions deal with massive datasets, ranging from customer transactions to market data. In the past, cleaning this data required extensive manual work and rule-based algorithms that often missed nuances. LLMs can assist in identifying fraudulent transactions, cleaning duplicate financial records, and even predicting market movements by analyzing unstructured market reports or news articles.

3. E-commerce

In e-commerce, product listings often contain inconsistent data due to manual entry or differing data formats across platforms. LLMs are helping e-commerce giants like Amazon clean and standardize product data more efficiently by detecting duplicates and filling in missing information based on customer reviews or product descriptions.

Challenges and Limitations

While LLMs have shown significant potential in data cleaning, they are not without challenges.

Training Data Quality: The effectiveness of an LLM depends on the quality of the data it was trained on. Poorly trained models might perpetuate errors in data cleaning.

Resource-Intensive: LLMs require substantial computational resources to function, which can be a limitation for small to medium-sized enterprises.

Data Privacy: Since LLMs are often cloud-based, using them to clean sensitive datasets, such as financial or healthcare data, raises concerns about data privacy and security.

The Future of Data Cleaning with LLMs

The advancements in LLMs represent a paradigm shift in how data cleaning will be conducted moving forward. As these models become more efficient and accessible, businesses will increasingly rely on them to automate data preprocessing tasks. We can expect further improvements in imputation techniques, anomaly detection, and the handling of unstructured data, all driven by the power of LLMs.

By integrating LLMs into data pipelines, organizations can not only save time but also improve the accuracy and reliability of their data, resulting in more informed decision-making and enhanced business outcomes. As we move further into 2024, the role of LLMs in data cleaning is set to expand, making this an exciting space to watch.

Large Language Models are poised to revolutionize the field of data cleaning by automating and enhancing key processes. Their ability to understand context, handle unstructured data, and perform intelligent imputation offers a glimpse into the future of data preprocessing. While challenges remain, the potential benefits of LLMs in transforming data cleaning processes are undeniable, and businesses that harness this technology are likely to gain a competitive edge in the era of big data.

[jcmarchi]

Non-fiction books that explore AI's impact on society  - AI News

New Post has been published on https://thedigitalinsider.com/non-fiction-books-that-explore-ais-impact-on-society-ai-news/

Non-fiction books that explore AI's impact on society  - AI News

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Artificial Intelligence (AI) is code or technologies that perform complex calculations, an area that encompasses simulations, data processing and analytics.

AI has increasingly grown in importance, becoming a game changer in many industries, including healthcare, education and finance. The use of AI has been proven to double levels of effectiveness, efficiency and accuracy in many processes, and reduced cost in different market sectors. 

AI’s impact is being felt across the globe, so, it is important we understand the effects of AI on society and our daily lives. 

Better understanding of AI and all that it does and can mean can be gained from well-researched AI books.

Books on AI provide insights into the use and applications of AI. They describe the advancement of AI since its inception and how it has shaped society so far. In this article, we will be examining recommended best books on AI that focus on the societal implications.

For those who don’t have time to read entire books, book summary apps like Headway will be of help.

Book 1: “Superintelligence: Paths, Dangers, Strategies” by Nick Bostrom

Nick Bostrom is a Swedish philosopher with a background in computational neuroscience, logic and AI safety. 

In his book, Superintelligence, he talks about how AI  can surpass our current definitions of intelligence and the possibilities that might ensue.

Bostrom also talks about the possible risks to humanity if superintelligence is not managed properly, stating AI can easily become a threat to the entire human race if we exercise no control over the technology. 

Bostrom offers strategies that might curb existential risks, talks about how Al can be aligned with human values to reduce those risks and suggests teaching AI human values.

Superintelligence is recommended for anyone who is interested in knowing and understanding the implications of AI on humanity’s future.

Book 2: “AI Superpowers: China, Silicon Valley, and the New World Order” by Kai-Fu Lee

AI expert Kai-Fu Lee’s book, AI Superpowers: China, Silicon Valley, and the New World Order, examines the AI revolution and its impact so far, focusing on China and the USA. 

He concentrates on the competition between these two countries in AI and the various contributions to the advancement of the technology made by each. He highlights China’s advantage, thanks in part to its larger population. 

China’s significant investment so far in AI is discussed, and its chances of becoming a global leader in AI. Lee believes that cooperation between the countries will help shape the future of global power dynamics and therefore the economic development of the world.

In thes book, Lee states AI has the ability to transform economies by creating new job opportunities with massive impact on all sectors. 

If you are interested in knowing the geo-political and economic impacts of AI, this is one of the best books out there. 

Book 3: “Life 3.0: Being Human in the Age of Artificial Intelligence” by Max Tegmark

Max Tegmark’s Life 3.0 explores the concept of humans living in a world that is heavily influenced by AI. In the book, he talks about the concept of Life 3.0, a future where human existence and society will be shaped by AI. It focuses on many aspects of humanity including identity and creativity. 

Tegmark envisions a time where AI has the ability to reshape human existence. He also emphasises the need to follow ethical principles to ensure the safety and preservation of human life. 

Life 3.0 is a thought-provoking book that challenges readers to think deeply about the choices humanity may face as we progress into the AI era. 

It’s one of the best books to read if you are interested in the ethical and philosophical discussions surrounding AI.

Book 4: “The Fourth Industrial Revolution” by Klaus Schwab

Klaus Martin Schwab is a German economist, mechanical engineer and founder of the World Economic Forum (WEF). He argues that machines are becoming smarter with every advance in technology and supports his arguments with evidence from previous revolutions in thinking and industry.

He explains that the current age – the fourth industrial revolution – is building on the third: with far-reaching consequences.

He states use of AI in technological advancement is crucial and that cybernetics can be used by AIs to change and shape the technological advances coming down the line towards us all.

This book is perfect if you are interested in AI-driven advancements in the fields of digital and technological growth. With this book, the role AI will play in the next phases of technological advancement will be better understood.

Book 5: “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy” by Cathy O’Neil

Cathy O’Neil’s book emphasises the harm that defective mathematical algorithms cause in judging human behaviour and character. The continual use of maths algorithms promotes harmful results and creates inequality.

An example given in  the book is of research that proved bias in voting choices caused by results from different search engines.

Similar examination is given to research that focused Facebook, where, by making newsfeeds appear on users’ timelines, political preferences could be affected.

This book is best suited for readers who want to adventure in the darker sides of AI that wouldn’t regularly be seen in mainstream news outlets.

Book 6: “The Age of Em: Work, Love, and Life when Robots Rule the Earth” by Robin Hanson

An associate professor of economics at George Mason University and a former researcher at the Future of Humanity Institute of Oxford University, Robin Hanson paints an imaginative picture of emulated human brains designed for robots. What if humans copied or “emulated” their brains and emotions and gave them to robots?

He argues that humans who become “Ems” (emulations) will become more dominant in the future workplace because of their higher productivity.

An intriguing book for fans of technology and those who love intelligent predictions of possible futures.

Book 7: “Architects of Intelligence: The truth about AI from the people building it” by Martin Ford

This book was drawn from interviews with AI experts and examines the struggles and possibilities of AI-driven industry.

If you want insights from people actively shaping the world, this book is right for you!

CONCLUSION

These books all have their unique perspectives but all point to one thing – the advantages of AI of today will have significant societal and technological impact. These books will give the reader glimpses into possible futures, with the effects of AI becoming more apparent over time.

For better insight into all aspects of AI, these books are the boosts you need to expand your knowledge. AI is advancing quickly, and these authors are some of the most respected in the field. Learn from the best with these choice reads.

[ego-sum-arbor]

Really not a fan of my dad’s tendency to go on about how autistic people statistically struggle with theory of mind and are therefore so callous and incapable of considering other people’s perspectives.

[shantitechnology]

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[antianimus]

honestly my favorite thing about reading or exploring new media thats personal to somebody is just going "I dont get this" in a personal experience perspective type of way and not that it was incoherent type of way.