#complete life cycle of a data science project

[analyticspursuit]

Stages of the Data Science Lifecycle: From Idea to Deployment

In this short video, we'll discuss the stages of the data science lifecycle, from idea to deployment. We'll cover the different stages of a data science project, from data collection to data analysis and finally, the presentation of the results.

If you're new to data science or you're looking for a better understanding of the life cycle of a data science project, then this video is for you!

By the end of this short video, you'll have a clearer understanding of the stages of the data science lifecycle, and you'll be able to move from idea to deployment with ease!

[mattved]

Mega projects

Two years ago, I changed jobs.

The role of BI Analyst that I moved from was about 80% hard-skill based. I took on it while living with a disability resulting from post-operation difficulties and it served me well, providing me with great work-life balance at acceptable wage. Thanks to the arrangement, I was able to take care of myself, deal with my physical handicap, and finally undergo a couple of surgeries, which restored my somewhat healthy status.

Following that, I finally started properly recovering mentally. Soon, I had been able to take on many of my previous hobbies other than gaming. I bought a bicycle, which I'd driven the 20 odd kilometers to work couple of times and even managed to climb the way to the dorms where my then girlfriend (now wife) stayed. I also managed to recondition and sell off computer hardware that I had had piling up for a good while then. I started new electronics projects and fixed appliences for friends and family. I redesigned my blog, made a website, designed a brandbook for an acquaintance, and edited hours of videos. It was almost as if I were back at the uni with little extra money, which allowed me to invest into stuff.

The next obvious thing to happen for any purpose-driven individual was becoming more proactive at work. I suggested expansion and overall improvement of architecture behind the firm's BI suite, because it was clearly necessary - more on that in an earlier article. In spite of my being categorized under finance, there was no real budget for it and most of my proposals ended up in an abbyss. I even paid for Google Cloud resources to automate some of the data science stuff.

When a new CTO came in and things finally started to move, he was more keen on bringing his own people to do the important work. Myself, being previously involved in projects of country-level importance including system implementation and process redesign, even being offered a similar role in the Netherlands, albeit shortly before being diagnosed with cancer, I felt it was unfair not to give me the opportunity. So I left to seek it elsewhere.

I found it with a firm two miles from my birthplace, which was founded some two years after I was born. [Coincidence? Likely.] They (or rather we) are a used car retailer and at that point in time needed to replace an old CRM system. And that's what I was tasked with, all the way from technology and supplier tender to the launch and establishment of iterative development cycles. It was notch up from what I did some time before then, exactly the challenge I felt I needed.

Supported by the director of ICT with profound experience working with a global logistics giant, I completed the implementation in two years. The role encompassed project management, across business stakeholders and external suppliers, creating technical specifications, but most of all, doing a lot of the programming myself - especially the integrations. Along the way, I was joined by a teammate, whom I slowly handed over the responsibility of overseeing the operations and providing L1 - L2 support.

The final 9 months leading up to that were particularly difficult, though - finalizing every little bit to the continually adjusting requirements put forward by the key process owners. In the week before go-live, I worked double hours to finish everything and enable a "big bang" transition. D-Day 3 am, I had to abort due to not making the final data migration in time, meaning that the switch happened on Valentine's day.

The extended care period, over which we had to fix every single bug and reduce glitches took about two more months. And even though we managed to present the whole thing as complete, oversight and further expansion still take about two days of my week.

Over the duration of the CRM project, I was fully invested in it and still managed to deliver some extras, like helping out with reporting, integrating Windows users repository with chip-based attendance system from late '00s, working with some weird APIs, and administering two servers loaded with devops utilities.

Personal life did not suffer entirely. I dedicated most to spending time with my girlfriend, even managed to marry her during that period. There were some home-improvement activities that needed to be done and a small number of hurried vacations. But all my side-projects and hobbies ended up being on hold.

And that is literally the only thing I regret about the project and, to date, from the whole job change. Now is the time to try and pick up where I left off. Regaining the momentum in writing and video editing will be particularly difficult. My wife wants me to help out with her cosplay, so I have good motivation to return to being crafty again and refresh the experience from when I made a LARP crossbow and melee weapons. Furthering home-improvement is a big desire of mine but cost is an issue nowadays, with rent and utilities being entirely on my shoulders.

And then there are two things that I want to achieve that I failed at for way too long. Obtaining a driver's license (or possibly making my wife get it) and losing weight. The latter, I am working on with the handy calorie tracking app that dine4fit.com is, especially in my current region, and my Garmin watch. We will hopefully go swimming again soon as well. The former is a whole different story surrounded by plenty of trauma that still needs some recovery and obviously the sacrifice of cost and time to complete it.

I believe I have now strongly improved my work-life balance, by far not to what I was used to at the uni, but to a level that should let me do things that I want to do. And I wish to maintain it for a while. Maybe before embarking on yet another mega project, albeit with a much better starting point than the one I had in this case? Who knows.

And about the money, I believe a spike will come eventually, with transition to another employer, most likely. But the longer I am here, the more experience comes my way in doses much greater than those I would get elsewhere if I were to move just now. I'm 28 and if I lose weight and make sure to overcome obstacles of personal nature, I will do better. As for not being a millionaire by the age of 30, I should be able to handle that.

I almost died five years ago, gave up on pursuing my master's, lost the chance to take on the opportunity I had in the Netherlands, and now live where I'd wished, even managed temporarily, to move away from. I do well understand how scarce our time is, but I have to cut myself some slack when others don't (upcoming article "cancer perks"). For what it is, I still rock, don't I?!

[juliebowie]

Your Guide to Learn about Data Science Life Cycle

Summary: This guide covers the Data Science Life Cycle's key stages, offering practical tips for managing data science projects efficiently. Learn how to transform raw data into actionable insights that drive business decisions.

Introduction

Understanding the Data Science Life Cycle is crucial for anyone looking to excel in data science. This cycle provides a structured approach to transforming raw data into actionable insights. By mastering each stage, you ensure that your data science projects are not only efficient but also yield accurate and meaningful results. 

This guide will walk you through the essential stages of the Data Science Life Cycle, explaining their significance and offering practical tips for each step. By the end, you’ll have a clear roadmap for managing data science projects from start to finish.

What is the Data Science Life Cycle?

The Data Science Life Cycle is a systematic approach to managing data science projects, guiding the entire process from understanding the problem to deploying the solution. 

It consists of several stages, including business understanding, data collection, data preparation, data exploration and analysis, modeling, and deployment. Each stage plays a critical role in transforming raw data into valuable insights that can drive decision-making.

Importance of the Life Cycle in Data Science Projects

Understanding the Data Science Life Cycle is essential for ensuring the success of any data-driven project. By following a structured process, data scientists can effectively manage complex tasks, from handling large datasets to building and refining predictive models. 

The life cycle ensures that each step is completed thoroughly, reducing the risk of errors and enhancing the reliability of the results.

Moreover, the iterative nature of the life cycle allows for continuous improvement. By revisiting earlier stages based on new insights or changes in requirements, data scientists can refine models and optimize outcomes. 

This adaptability is crucial in today’s dynamic business environment, where data-driven decisions must be both accurate and timely. Ultimately, the Data Science Life Cycle enables teams to deliver actionable insights that align with business goals, maximizing the value of data science efforts.

Read: Data Destruction: A Comprehensive Guide.

Stages of the Data Science Life Cycle

The Data Science Life Cycle is a systematic process that guides data scientists in turning raw data into meaningful insights. Each stage of the life cycle plays a crucial role in ensuring the success of a data science project. 

This guide will walk you through each stage, from understanding the business problem to communicating the results effectively. By following these stages, you can streamline your approach and ensure that your data science projects are aligned with business goals and deliver actionable outcomes.

Business Understanding

The first stage of the Data Science Life Cycle is Business Understanding. It involves identifying the problem or business objective that needs to be addressed. 

This stage is critical because it sets the direction for the entire project. Without a clear understanding of the business problem, data science efforts may be misaligned, leading to wasted resources and suboptimal results.

To begin, you must collaborate closely with stakeholders to define the problem or objective clearly. Ask questions like, "What is the business trying to achieve?" and "What are the pain points that data science can address?" 

This helps in framing the data science goals that align with the broader business goals. Once the problem is well-defined, you can translate it into data science objectives, which guide the subsequent stages of the life cycle.

Data Collection

With a clear understanding of the business problem, the next step is Data Collection. In this stage, you gather relevant data from various sources that will help address the business objective. Data can come from multiple channels, such as databases, APIs, sensors, social media, and more.

Understanding the types of data available is crucial. Data can be structured, such as spreadsheets and databases, or unstructured, such as text, images, and videos. 

Additionally, you must be aware of the formats in which data is stored, as this will impact how you process and analyze it later. The quality and relevance of the data you collect are paramount, as poor data quality can lead to inaccurate insights and faulty decision-making.

Data Preparation

Once you have gathered the data, the next stage is Data Preparation. This stage involves cleaning and preprocessing the data to ensure it is ready for analysis. 

Data cleaning addresses issues such as missing data, outliers, and anomalies that could skew the analysis. Preprocessing may involve normalizing data, encoding categorical variables, and transforming data into a format suitable for modeling.

Handling missing data is one of the critical tasks in this stage. You might choose to impute missing values, drop incomplete records, or use algorithms that can handle missing data. 

Additionally, outliers and anomalies need to be identified and addressed, as they can distort the results. Data transformation and feature engineering are also part of this stage, where you create new features or modify existing ones to better capture the underlying patterns in the data.

Data Exploration and Analysis

Data Exploration and Analysis is where you begin to uncover the insights hidden within the data. This stage involves conducting exploratory data analysis (EDA) to identify patterns, correlations, and trends. EDA is an essential step because it helps you understand the data's structure and relationships before applying any modeling techniques.

During this stage, you might use various tools and techniques for data visualization, such as histograms, scatter plots, and heatmaps, to explore the data. These visualizations can reveal important insights, such as the distribution of variables, correlations between features, and potential areas for further investigation. 

EDA also helps in identifying any remaining issues in the data, such as biases or imbalances, which need to be addressed before moving on to the modeling stage.

Data Modeling

After exploring the data, the next stage is Data Modeling. This stage involves selecting appropriate algorithms and models that can help solve the business problem. The choice of model depends on the nature of the data and the specific problem you are trying to address, whether it's classification, regression, clustering, or another type of analysis.

Once you've selected the model, you'll train it on your data, testing different configurations and parameters to optimize performance. After training, it's crucial to evaluate the model's performance using metrics such as accuracy, precision, recall, and F1 score. This evaluation helps determine whether the model is robust and reliable enough for deployment.

Model Deployment

With a well-trained model in hand, the next step is Model Deployment. In this stage, you deploy the model into a production environment where it can start generating predictions and insights in real-time. Deployment may involve integrating the model with existing systems or setting up a new infrastructure to support it.

Continuous monitoring and maintenance of the model are vital to ensure it remains effective over time. Models can degrade due to changes in data patterns or shifts in business needs, so regular updates and retraining may be necessary. Addressing these issues promptly ensures that the model continues to deliver accurate and relevant results.

Communication of Results

The final stage of the Data Science Life Cycle is Communication of Results. Once the model is deployed and generating insights, it's essential to communicate these findings to stakeholders effectively. Visualization plays a key role in this stage, as it helps translate complex data into understandable insights.

You should present the results clearly, highlighting the key findings and their implications for the business. Creating actionable recommendations based on these insights is crucial for driving decision-making and ensuring that the data science project delivers tangible value.

Challenges in the Data Science Life Cycle

In the Data Science Life Cycle, challenges are inevitable and can arise at every stage. These obstacles can hinder progress if not addressed effectively. However, by recognizing common issues and implementing strategies to overcome them, you can ensure a smoother process and successful project outcomes.

Data Collection Issues: Incomplete or inconsistent data can disrupt analysis. To overcome this, establish clear data collection protocols and use automated tools for consistency.

Data Quality Problems: Poor data quality, including missing values or outliers, can lead to inaccurate models. Regular data cleaning and validation checks are crucial to maintain data integrity.

Complex Data Modeling: Selecting the right model and parameters can be challenging. To tackle this, experiment with different algorithms, and consider cross-validation techniques to refine your model.

Deployment Hurdles: Deploying models into production environments can face integration issues. Collaborate with IT teams and ensure proper infrastructure is in place for smooth deployment.

Communication Barriers: Effectively communicating insights to non-technical stakeholders is often difficult. Use visualizations and clear language to bridge this gap.

Collaboration and domain knowledge play a pivotal role in addressing these challenges. By working closely with domain experts and cross-functional teams, you can ensure that the data science project aligns with business objectives and achieves desired outcomes.

Best Practices for Managing the Data Science Life Cycle

Managing the Data Science Life Cycle effectively is crucial for ensuring project success and delivering actionable insights. By following best practices, you can streamline processes, enhance collaboration, and achieve better results. Here are some key tips for efficient project management in data science:

Prioritize Clear Communication: Maintain open channels of communication among team members to ensure everyone is aligned with project goals and timelines. Regular check-ins and updates help in addressing issues promptly.

Document Every Step: Proper documentation is essential for tracking progress and maintaining a clear record of decisions and methodologies. This not only aids in replication and debugging but also provides a reference for future projects.

Emphasize Version Control: Use version control systems like Git to manage changes in code, data, and models. This ensures that all team members work on the latest versions and can easily revert to previous states if necessary.

Choose the Right Tools and Technologies: Selecting appropriate tools for each stage of the data science life cycle is vital. Use powerful data processing tools like Python or R, data visualization tools like Tableau, and cloud platforms like AWS for scalability and efficiency.

By implementing these best practices, you can manage the Data Science Life Cycle more effectively, leading to more successful and impactful data science projects.

Frequently Asked Questions

What are the stages of the Data Science Life Cycle?

The Data Science Life Cycle consists of six key stages: Business Understanding, Data Collection, Data Preparation, Data Exploration and Analysis, Data Modeling, and Model Deployment. Each stage plays a crucial role in transforming raw data into actionable insights.

Why is the Data Science Life Cycle important?

The Data Science Life Cycle is essential because it provides a structured approach to managing data science projects. By following each stage methodically, data scientists can ensure accurate results, efficient workflows, and actionable outcomes aligned with business goals.

How does Data Exploration differ from Data Modeling?

Data Exploration involves analyzing and visualizing data to uncover patterns, correlations, and trends, while Data Modeling focuses on selecting and training algorithms to create predictive models. Exploration helps understand the data, and modeling leverages this understanding to solve specific business problems.

Conclusion

Mastering the Data Science Life Cycle is key to driving successful data-driven projects. 

By understanding and meticulously following each stage—from business understanding to communication of results—you can ensure that your data science efforts yield accurate, actionable insights. Implementing best practices, such as clear communication, documentation, and tool selection, further enhances the efficiency and impact of your projects.

[govindhtech]

IBM Cloud HPC: Cadence-Enabled Next-Gen Electronic Design

Cadence makes use of IBM Cloud HPC

With more than 30 years of experience in computational software, Cadence is a leading global developer in electrical design automation (EDA). Chips and other electrical devices that power today’s growing technology are designed with its assistance by businesses all over the world. The company’s need for computational capacity is at an all-time high due to the increased need for more chips and the integration of AI and machine learning into its EDA processes. Solutions that allow workloads to move between on-premises and cloud environments seamlessly and allow for project-specific customization are essential for EDA companies such as Cadence.

Chip and system design software development requires creative solutions, strong computational resources, and cutting-edge security support. Cadence leverages IBM Cloud HPC with IBM Spectrum LSF as the task scheduler. Cadence claims faster time-to-solution, better performance, lower costs, and easier workload control using IBM Cloud HPC.

Cadence also knows personally that a cloud migration may call for new skills and expertise that not every business has. The goal of the entire Cadence Cloud portfolio is to assist clients worldwide in taking advantage of the cloud’s potential. Cadence Managed Cloud Service is a turnkey solution perfect for startups and small- to medium-sized businesses, while Cloud Passport, a customer-managed cloud option, enables Cadence tools for large enterprise clients.

Cadence’s mission is to make the cloud simple for its clients by putting them in touch with experienced service providers like IBM, whose platforms may be utilized to install Cadence solutions in cloud environments. The Cadence Cloud Passport approach can provide access to cloud-ready software solutions for usage on IBM Cloud, which is beneficial for organizations looking to accelerate innovation at scale.

Businesses have complicated, computational problems that need to be solved quickly in today’s cutthroat business climate. Such issues may be too complex for a single system to manage, or they may take a long time to fix. For businesses that require prompt responses, every minute matters. Problems cannot fester for weeks or months for companies that wish to stay competitive. Companies in semiconductors, life sciences, healthcare, financial services, and more have embraced HPC to solve these problems.

Businesses can benefit from the speed and performance that come with powerful computers cooperating by utilizing HPC. This can be especially useful in light of the ongoing desire to develop AI on an ever-larger scale. Although analyzing vast amounts of data may seem unfeasible, high-performance computing (HPC) makes it possible to employ powerful computer resources that can complete numerous calculations quickly and concurrently, giving organizations access to insights more quickly. HPC is also used to assist companies in launching innovative products. Businesses are using it more frequently because it helps manage risks more effectively and for other purposes.

HPC in the Cloud

Businesses that operate workloads during periods of high activity frequently discover that they are running above the available compute capacity on-premises. This exemplifies how cloud computing may enhance on-premises HPC to revolutionize the way a firm uses cloud resources for HPC. Cloud computing can help with demand peaks that occur throughout product development cycles, which can vary in length. It can also give businesses access to resources and capabilities that they may not require continuously. Increased flexibility, improved scalability, improved agility, increased cost efficiency, and more are available to businesses who use HPC from the cloud.

Using a hybrid cloud to handle HPC

In the past, HPC systems were developed on-site. But the huge models and heavy workloads of today are frequently incompatible with the hardware that most businesses have on-site. Numerous organizations’ have turned to cloud infrastructure providers who have already made significant investments in their hardware due to the large upfront costs associated with acquiring GPUs, CPUs, networking, and creating the data Centre infrastructures required to effectively operate computation at scale.

A lot of businesses are implementing hybrid cloud architectures that concentrate on the intricacies of converting a section of their on-premises data Centre into private cloud infrastructure in order to fully realize the benefits of both public cloud and on-premises infrastructures.

Employing a hybrid cloud strategy for HPC, which combines on-premises and cloud computing, enables enterprises to leverage the advantages of each and achieve the security, agility, and adaptability needed to fulfil their needs. IBM Cloud HPC, for instance, may assist businesses in managing compute-intensive workloads on-site flexibly. IBM Cloud HPC helps enterprises manage third- and fourth-party risks by enabling them to use HPC as a fully managed service, all while including security and controls within the platform.

Looking forward

Businesses can overcome many of their most challenging problems by utilizing hybrid cloud services via platforms such as IBM Cloud HPC. Organization’s adopting HPC should think about how a hybrid cloud strategy might support traditional on-premises HPC infrastructure deployments.

Read more on Govindhtech.com

[amitypunjab]

Tech Titans: Discovering the Best Computer Application College in Punjab

Bachelor's of computer application focuses on computer application and software development. The top computer application college in Punjab, Amity University has this course of 3 years. It provides students with a strong foundation in computer science and software development.

Some key aspects covered in a BCA program are:

Software Engineering:

Software Development Life Cycle (SDLC)

Software Testing and Quality Assurance

Core Computer Science Subjects:

Data Structures

Algorithms

Computer Networks

Operating Systems

Programming Languages:

C

C++

Java

Python

Web Technologies:

HTML, CSS, JavaScript

Server-side scripting languages (e.g., PHP, ASP.NET)

Graduates of BCA programs can work in a variety of IT-related fields, such as database management, system analysis, software development, and more. The curriculum prepares students for professions in the quickly developing sector of information technology by bridging the gap between theoretical understanding and practical application.

Bachelor of Computer Applications (Honours / Research)

There are many Undergraduate Programs at Amity University Punjab, one being Bachelor of Computer Applications (Honours / Research). 

BCA(Honours) 

A BCA (Honours) program usually means that academic rigor, research, and specialization are given more weight. BCA (Honors) students may take on more difficult coursework, work on more complex projects, and study computer applications in greater detail.

BCA(Research)

The completion of a research thesis or dissertation, project work, and research methodology may receive more attention in a BCA (Research) program. This kind of curriculum is meant to develop research abilities, thus it can be appropriate for students who want to get involved in the scientific or business research community.

Let's have a look at some of the features of the program:

Research Projects

It can be necessary for students to work on research projects or take part in ongoing studies, which could result in presentations or publications.

Seminar Series

The program includes series, seminars, conferences, and workshops, where students share their research findings and advancements in the fields. 

Internships

Internships are a very important part of the students for the practical exposure and theoretical knowledge gained during the program.

The structure and criteria of the BCA program differ, therefore it's important to confirm the specifics supplied by the university or institution of interest. So while making your choice, do a thorough research for the same. Know more

Best Computer Application College in Punjab Source: https://topcollegepunjab.blogspot.com/2023/12/tech-titans-discovering-best-computer.html

[yther]

I'm like if a person Survived the Horrors, again and again

(and is a trapdoor spider)

which is both good and bad... and good and better

I'm also that one song White and Nerdy, but which everyone still hears (wants to hear?) as Ridin' Dirty. Unfortunately,,,, I am not an audiologist

so I accept this fact: that misinterpretations exist in others. I accept this knowing it is not my business, or my personal responsibility to attempt to correct hasty opinions or what I believe to be (mis)judgements.

I understand and accept that I don't control perceptions, and I acknowledge that I've been highly reactive directly in spite of this fact, and that I reverted back to being feral child.

None of this negates the wrongness and complete depravity with which I was ever treated. I did not deserve this. There *isn't* any blood on these hands. If ever, it was my own.

I remember, despite my memory issues, that "to be gracious in this body" is one of the best poems I've written. I recognize I am so far from grounded, and I remember why. And I remember that nostalgia is a liar.

And I learn. Perhaps not the lesson anticipated, much to some person's discontent. Inane or insane, to persist. Simpletons who've read 15 reductive platitudes (edit: witticism) or quotes from Top 10 Smort Humans might be leik mmm oh! the guy said trying something twice and expecting a different result is the definition of stupidity (“Insanity is doing the same thing over and over and expecting different results.”) and I just--

mmm babe, you're quoting Einstein yeah. Scientist yeah. Complex life and witness to horrors of humanity, very true. But um. Guess how science functions as pertains to redundancy and bad data (former being in prevention, latter as identifying and discarding) though! And also. Fuck if his genius ever particularly applied to conventions and technologies of the digital age and interpersonal relations OR politics in this landscape like, theologically this is an assertion so. Really. Okay. Learn to recontextualize overly broad shit instead of leaning on it for "thoughtful" interpretation. ...Or don't.

This is not my problem. Absurdity, cruelty, and injustice are the facts. I accept what is being done to me without an understanding of "why" because I accept that there isn't meaning in everything. There exists within us an innate animal instinct, and Society is a tentative construct or whatever. I see purposefulness in the actions of others

and the situations in my external life

because I am looking for it, projecting it, attempting to embody it or manifest it because

greater purpose

is what I seek, what I envision, what I believe in. but that's just an idea held by me.

&so / ERGO

I understand, like many human perceptions, any and every type of "ideology" is slippery, and dangerous when tangled up in..as it becomes a noose. They (ideas of intention and meaning) reflect my inner life and not reality, certainly not the reality perception of others.

And I accept loss, though I don't see it as similar to defeat. Life is not a war- humans can create that story and reread it for thousands of years but that doesn't make it true outside of an anthropomorphous view of the entire fucking universe. Life is cycles, life has birth and death and a type of violence. But it is not vengeance, it is not a crusade, it is not m o t i v a t e d in any sense that relates back to being human. Are we "defeated" by death? Hunger? Or do they just exist and occur as inevitabilities? Simply, the facts of life. like ego = suffering. Are these things ever something we can battle? Of course not. Thus, Change and impermanence, tho personally perceived as loss, being a natural force, is not an enemy. We can assign it no deeper meaning.

The human behaviors --- what's driving individuals to hurt Others or Another --- is a force of nature as/in humans, not any true opponent. Not something with which you can attach an enmity or greater purpose. Neither sacred nor sinful.

The quality of our tension is not personal to anyone. ((but the self, if ever. suffering by our expectation, etc.)) haters create hate. hate does not exist outside of the human mind. there is no latent hate. nature is no She. the universe does not hold any hatred. there is no intent to the processes. there is no resolution or conclusion. possible to influence or manipulate the environment? yes! but it is still only affected in a sense that is human-spun - threaded with our own inventions of cause.

Even purposeful manipulation of one human psyche by another is not personal. I accept that concepts of defeat or winning are words weighted with human conception- in a sense, I (can only) personally accept defeat whereupon it is made personal. and I imbue it with story, meaning.

It is my choice, (my power seulement) whether I "win" or "lose", because this battlement and barricade, if I should call it that, is immaterial. War is not at the gates, Openly! whether or not I would risk it. this is not the shared human-made narrative LOTR.

There is no you,, that I know-recognize-sight truly. There is only the question of: me.

&everyone&& (if there should be an ampersand at all, if there is I/You or I/Otherness). The universe will answer the questions we ask. And if I should meet it in silence, then I hear no echo. If I am listening for shouting or shouting into the void then - shouts I hear.

I accept that I will not always remember this and my reactivity to the external world is subject to change. I will fail, at some points, and in certain capacities, and possibly without prediction. As is natural law. As is, nay- may be, bound in human's own law and ethics.

Fortitude and flexibility can be both active and passive. Indomitable as a trait must adhere to flux, paradoxically.

Indomitable spirit, a sisu relative, a steadfastness vs stubborness: I assert this all persists in the space betwe e n.

(between)And -

(&)

yet!

(and yet!!)

I exist - if in the face of friction - - - - I do persist without necessitating qualms. It (this coming into paradox) is adamantly beside itself, semantically and otherwise. I exist in this state both actively passive and passively active. To witness and to perceive. To judge and react. All assertions from the self, each a separate fulcrum on which We may build a Crux.

My life is ever only retaliatory in my own adorned escapades I seek, online/in disguised actions. I don't see or hear you unless I choose. You don't exist to me, if I choose. The physical material pending. The real consequence pending, pending the fruition of threats. Humiliation is layers of applied perceptions and judgements. Like the concept of Others. And in this sense it is imagination.

How I imagine. Who else I might imagine within my own inner monologue is my prerogative in regards to letting in and whether I stay and sit with it or let it pass. It is not ignorance or denial or haughtiness or some sense of dispassionate solipsism. It is a boundary without convention. It may breathe, as do I.

As I will do. As I continue, without spite of any kind ~tilde

&I am laughing about IT. Life. And I am crying. And DANCING like however I do and will continue to d a n s e, in whichever way I might be moved to move. As is my prerogative, again. As is my joy, my freedom, my right s.

As I write what will be an overly long text post to..._ Mm. Fits just fine on my blog. In a text box. By what imaginations am I making it "overly" anything? By weaponizing conscientiousness and convention when in the reality:

Only I am here. (This is my fucking blog, ffs. The audience I cater to is nil. and null&voidkitty.)

It is online but invisible. [callback to paradox of actively passive/passively active vs Active and Passivity as qualifiers for reactivity, or indicative of amount of space given to a thought or circumstance. Amount of actions may be pedantically inflated or diminished. The amount of my being is beyond that which the world will know. As to vrai (nei^ei) true-true know a person's inner experience and sensations is impossible, philosophically and scientifically atm.]

signed, def not Kafka. Obviously! (?) this is known!! ------- - - > said in pained human song of shitty soliloquies.

p.s. Coda : (is to! istu? ei)

Codes :: tail : tail

\Or-

/Or-

Ouroboros

[jcmarchi]

Scientists Using Supercomputers to Scale Up Their Computation Level - Technology Org

New Post has been published on https://thedigitalinsider.com/scientists-using-supercomputers-to-scale-up-their-computation-level-technology-org/

Scientists Using Supercomputers to Scale Up Their Computation Level - Technology Org

What will the future bring for U.S. scientists using supercomputers to scale up their computations to the highest level? And what technologies should cyberinfrastructure providers deploy to match their ambitions?

Paul Woodward, University of Minnesota, describes star convection simulations using TACC’s Frontera supercomputer. Image Credit: TACC.

These questions and more were explored at the 3rd annual Frontera User Meeting August 3-4, 2023, held at the Texas Advanced Computing Center (TACC).

“It’s a great opportunity to hear about how Frontera is performing and for users to hear from each other about how they’re maximizing the system,” said Dan Stanzione, executive director of TACC and the principal investigator of the National Science Foundation (NSF)-funded Frontera supercomputer.

Frontera is the most powerful supercomputer ever deployed by the NSF, and it’s the fastest U.S. academic system according to the latest (June 2023) Top500 rankings. Frontera serves as the leading capability system in the national cyberinfrastructure intended for large applications that require thousands of compute nodes.

Scientists tour the data center with TACC’s Frontera supercomputer. Image Credit: TACC.

Over the past 12 months, Frontera has provided rock steady service with 99 percent uptime and an average continuous utilization of 95 percent of its cycles. It delivered more than 72 million node hours and completed over one million jobs, with a cumulative completion of more than 5.8 million jobs over its four years of life.

As of September 2023, Frontera has progressed through more than 80 percent of its projected lifespan with new technology coming that will extend its operation through late 2025.

Approximately 30 scientists participated in the 2023 Frontera User Meeting. The event featured 13 invited speakers who shared their recent experiences and findings while utilizing Frontera.

The presentations included projects taking advantage of the many ways that users get allocations on the system. Some focused on smaller “startup” activities for groups beginning the transition to very large-scale computing. Others, such as the Large-Scale Community Partnership allocation, are long-term collaborations with major experimental facilities and require over a million node-hours of computing resources.

Other presentations focused on more extensive initiatives, such as the Leadership Resource Allocations, which received up to five million node-hours of computational support. Additionally, certain awardees, known as Texascale Days recipients, were granted access to Frontera’s full capacity, including its impressive 8,000+ nodes.

The presentations encompassed many domains of science ranging from cosmology to hurricanes, earthquakes to the memory center of the human brain, and more. All credited the unprecedented access to computing resources at the scale provided by Frontera as a cornerstone in allowing new understanding and discoveries in cutting-edge research.

Hurricane Storm Surge

Eirik Valseth, a research associate in the Computational Hydraulics Group, Oden Institute of UT Austin, described new work on Frontera to develop compound storm surge models that add river flooding effects with extreme resolution for the Texas coast.

His group is also using Frontera to generate five-day hindcast and seven-day forecasts for global ocean storm surge in collaboration with The University of Notre Dame and the U.S. National Oceanic and Atmospheric Administration, in efforts to allow better planning for hurricanes.

Simulation snapshot generated by Frontera of new model that combines storm surge and river flooding data along the Texas coast. Image Credit: Eirik Valseth, Oden Institute.

Big One Along the San Andreas Fault

Yifeng Cui, the director of the High Performance GeoComputing Laboratory at the San Diego Supercomputer Center (SDSC), described nonlinear earthquake simulations performed by his team on Frontera during TACC’s Texascale Days.

The simulations scaled up to 7,680 nodes and ran for 22.5 hours to simulate 83 seconds of shaking during a magnitude 7.8 quake on the southern San Andreas fault. More accurate simulations allow communities to plan better to withstand these large earthquakes, thus saving lives and property.

Snapshot of ShakeOut Scenario simulations on Frontera of M7.8 Earthquake on Southern San Andreas Fault. Image Credit: Yifeng Cui, San Diego Supercomputer Center.

Child Brain Development

Jessica Church-Lang, an associate professor in the Department of Psychology at UT Austin, is using Frontera to analyze anonymized fMRI image data of brain activity in children to find connections between its various systems including control, visual, motor, auditory, and more.

Frontera has helped her to construct 3D brain models from the fMRI images. “It takes about five hours, per child, on Frontera to run the analysis. It used to take three days on older computers. And this is just one step of our processing pipeline.”

Brain Bubbles

Frontera is helping scientists probe the mysteries of how the brain forms thoughts in research led by Jose Rizo-Rey, a professor of biophysics at UT Southwestern Medical Center. His research, using all-atom molecular dynamics simulations on Frontera, investigates tiny bubbles called “vesicles” that shuttle neurotransmitters across the gap between neurons, carrying the signal the brain uses to communicate with itself and other parts of the body.

“The process of fusion can happen in just a few micro seconds,” Rizo-Rey said. “That’s why we hope that we can simulate this with Frontera.”

Simulation of vesicle-flat bilayer interface of membrane fusion. Image Credit: Jose Rizo-Rey, University of Texas Southwestern Medical Center.

Memories, Models, and Optimizations

Research Engineer Ivan Raikov, Department of Neurosurgery at Stanford University, presented his progress on developing a large-scale model of the rodent hippocampus, a region of the brain associated with short-term memory and spatial navigation.

The project is creating the first-of-its-kind biophysically detailed, full scale model of the hippocampal formation with as close as possible to a 1-to-1 scale representation of every neuron. “We start with a full-scale hippocampal model with one million neurons,” Raikov said. “It takes about six hours to simulate 10 seconds of hippocampal activity on 1,024 nodes of Frontera.”

Turbulent Times

P.K. Yeung, professor of aerospace engineering at Georgia Tech, presented his work using Frontera to study turbulent dispersion, an example of which is the spread of a candle’s smoke or how far disease agents travel through the atmosphere.

Yeung’s simulations on Frontera track the motion of systems of more than a billion particles, calculating the trajectory and acceleration of each fluid element passing through a turbulent, high-rotation zone in what is known as Lagrangian intermittency in turbulence.

Sample trajectory and acceleration of fluid element passing through high-rotation zone illustrating Lagrangian intermittency in fluid turbulence. Image Credit: PK Yeung, Georgia Tech.

Star Turnover

Paul Woodward, the director of the Laboratory for Computational Science & Engineering and a professor in the School of Physics and Astronomy, University of Minnesota, performed 3D hydrodynamical simulations on runs of up to 3,510 compute nodes on Frontera of rotating, massive, main sequence stars to study convection in the interior of the star.

“Frontera is powerful enough to permit us to run our non-rotating simulation forward in time for about three years, which is an amazing thing to have done,” Woodward said.

Black Hole Cosmology

Simeon Bird, an assistant professor in the Department of Physics & Astronomy, UC Riverside, presented a new suite of cosmological simulations called PRIYA (Sanskrit for ‘beloved’). The PRIYA simulations performed on Frontera are some of the largest cosmological simulations performed, needing over 100,000 core-hours to simulate a system of 3072^3 (about 29 billion) particles in a ‘box’ 120 megaparsecs on edge, or about 3.91 million light years across.

“We run multiple models, interpolate them together and compare them to observational data of the real universe such as from the Sloan Digital Sky Survey and the Dark Energy Spectroscopic Instrument,” Bird said.

The PRIYA cosmological suite developed on Frontera incorporates multiple models with different parameters to form some of the largest cosmological simulations to date. Image Credit: Simeon Bird, UC Riverside.

Space Plasma

Half of all the universe’s matter — composed of protons and neutrons — resides in space as plasma. The solar wind from stars such as our sun shapes clouds of space plasma. And on a much larger scale, cosmic magnetic fields knead space plasma across galaxies.

“Some of our recently published work has made use of Frontera to study the turbulent dynamos in conducting plasma, which amplify cosmic magnetic fields and could help answer the question of the origin of magnetic fields in the universe,” said graduate student Michael Zhang, Princeton Program in Plasma Physics, Princeton University.

Tight Junctions

Tight junctions are multiprotein complexes in cells that control the permeability of ions and small molecules between cells, as well as supporting transport of nutrients, ions, and water. Sarah McGuinness, a PhD candidate in biomedical engineering at the University of Illinois, Chicago, presented progress using molecular dynamics simulations on Frontera to research Claudin-15, a protein which polymerizes into strands to form the backbone of tight junctions.

“Computational simulations allow investigators to observe protein dynamics and atomic resolution with resources like Frontera,” McGuinness said.

Protein Sequencing

Behzad Mehrafrooz, a PhD student at the Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, outlined his group’s latest work extending the reach of nanopores to sequence entire proteins, which are much larger and more complex than DNA.

“Thanks to Frontera, it was one of the longest, if not the longest molecular dynamics simulations for nanopore sequencing yet made,” Mehrafrooz said. “And it confirmed the rapid, unidirectional translocation induced by guanidinium chloride and helped unravel the molecular mechanism behind it.”

Viral Packaging

Kush Coshic, a PhD student in the Aksimentiev Lab at the University of Illinois at Urbana-Champaign, described simulations that took more than four months to perform using Frontera’s GPU nodes to simulate the genomic packaging of a model herpes-like virus, applicable to developing new therapeutics. “Frontera enables us to perform unprecedented high throughput analysis of a 27 million atom system,” Coshic said.

Spectral Function

“We’ve developed a new algorithm for calculating spectral functions with continuous momentum resolution that complements existing many-body techniques,” said Edwin Huang, an assistant professor in the Department of Physics & Astronomy at Notre Dame University.

His team’s determinantal quantum Monte Carlo solver for computing the spectral function of fermionic models with local interactions required sampling over a billion state configurations on Frontera.

Path to Horizon

Planning is underway for a massive new system as part of the NSF-funded Leadership Class Computing Facility (LCCF), with a projected 10X the capabilities of Frontera. Early users of the new system, called Horizon, can expect it to start in the second half of 2025 and enter full production in 2026.

“There are still opportunities to talk about what goes into Horizon,” Stanzione said. “One of the points of this meeting is to continue requirement gathering.”

To unlock the potential of Horizon, the future system will need to provide robust support for both CPU- and GPU-based codes. Software performance directions being explored are mixed precision matrix operations in GPUs, which can offer a 30X advantage in performance over single precision vector units.

“Software enables science and it will drive our decisions about future systems. The most important thing for TACC is that we get to hear from users about what is working with Frontera, what can be improved, and what needs to change to meet their needs in future systems,” Stanzione concluded.

Source: TACC

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[thegravityblog]

Monday: 19-06-2023 - Work!

Monday’s are always so jammed up. Started my day by picking up my dad from the diagnostic center. Unfortunately, he’s got a stone in his gall bladder which is 30mm in size, hence he has to undergo either a surgery or a laparoscopy operation. Anyway, then dived into reading news, looking up to what’s going on in the tech industry, world economy and markets.  Logged into zerodha, checked how the companies are performing and then dived into Dexa. In talks with a couple of angel investors to raise our seed round. Will pitch an angel in-person this Friday, hoping to crack it. After the calls, had another internal team call, assigning my team their weekly tasks. Building Dexa’s notification center that’ll trigger once you purchase/mint a ticket. Ticket minting works like charm, and you can also try by minting a ticket for the Web3 Meetup Chandigarh.  Post lunch I practiced Javascript till now. Revising is very important, and I solved problems starting from loops, conditions, if/else, Booleans, statements etc. Programming is best learned while doing. You can cram up all the concepts theoretically but it all boils down to execution. I also practice a lot of LeetCode problems. Also, I am participating in a data science competition at Numerai.   Numerai is a data science competition where you build machine learning models to predict the stock market. 

I am also betting big on CRISPR. I have read around 70 hours of books, research papers and medical apprehensions. I am sure I will make a hell lot of money through this. It’ll take time, but that’s certain.  Also: Live below your means. I hardly spend now. My CA has blocked all my shopping expenses for this month because last month I already have for my birthday. By 2024 I’ll hit 1.5 crores in revenue (projected) and till then, living like a student. Stashing the bank and a lil lifestyle upgrade that’s not even noticeable. 

Post tea in the evening, checked my NASDAQ portfolio. Eager for the next quarter as NVIDIA is a bold bet I have got my hands into. I’ve put a huge chunk on NVIDIA, and I will pull out and neutralize before the bubble bursts. I’ll write about the market cycles once the next quarter results are out.  Spoke to the lady, I am dating for a while. We both are busy professionals so there’s no “Nibba Nibbi” shit going on here. :D We just check up on each other, see if both of us are doing well, ask how our day was, if we need help, and motivate each other. She’s ambitious too, as I have mentioned earlier as well, so our wavelengths match. We don’t argue on how we are so busy, yes we are because we strive to make it big, and it’s normal. Both of us are around the same age, and its normal that we have other shit to do, apart from each other. 

Pre-tea lifted weights. 10 kg, 60 60 sets each. Arms are like jacked up but used to it. I’ll gain weight soon, once my treatment is over. Still recovering from the years of abuse I did to my body. Will code for a while, check up on the markets, listen to updates, check the crypto portfolio, read the book I have been reading (Peter Lynch’s Beating the Street) and then sleep. For me a 6-7 hour sleep works to stay laser focused and work like hell. And that bitch wants attention, well I am so done with her that I won’t even write more than this. I now entertain people who are equal to me, in class or if they have a good character and are good human beings otherwise, nope. That is why I am very selective about people. I don’t entertain crass anymore. These type of idiots are living in their hypocritical imaginative life, where they talk so big, but are exactly the opposite. They live in this la la land of success but are not even real. I don’t take them seriously, in fact anyone with values and principles would ignore their existence completely. What work have they done, that’s noticeable? Appreciated? What are they contributing to the society? How much are they contributing to our economy? What change are they bringing to this world? NOTHING. Hence, I have no space for these people. Ignore.  Well, that’s pretty much it. Let’s get back to work, read and then sleep! 

[walkswithjuniper]

An Introduction - Budburst

Website:

The Issue:

The cherry blossoms were going to peak early this year, and news sources from all over the capital region highlighted this as a harbinger of climate change. While an earlier Cherry BlossomFest may not seem like a big deal, last year saw Kyoto, Japan record their earliest peak bloom in 1,200 years (Christidis et al., 2022) (Primack et al., 2023). While cherry blossom trees are known to be very sensitive to temperature this, this issue has been observed and studied for years in the field of phenology - the timing of biological events - in numerous plants and animals.

Plants, especially wildflowers and native plants, face a number of environmental challenges, including changes in temperature and precipitation patterns, habitat loss and fragmentation, and increased competition from invasive species; for which we may see declines in plant populations, reduced genetic diversity in plants, reduced pollination, and overall an altered ecosystem (Willis et al., 2008) (Willis et al., 2010) (Inouye, 2022).

This is where Budburst comes in. Using phenology in the analysis of local plants over time, scientists are able to track the effects of climate change in cross-walking the data compiled by Budburst participants and other open-source information such as temperature and precipitation records. It also allows for the identification and tracking of the spread of invasive plant species.

Citations:

Christidis, Nikolaos, et al. “Human Influence Increases the Likelihood of Extremely Early Cherry Tree Flowering in Kyoto.” Environmental Research Letters, vol. 17, no. 5, May 2022, p. 054051. Institute of Physics, https://doi.org/10.1088/1748-9326/ac6bb4.

Inouye, David W. “Climate Change and Phenology.” WIREs Climate Change, vol. 13, no. 3, 2022, p. e764. Wiley Online Library, https://doi.org/10.1002/wcc.764.

Primack, Richard, et al. “How Climate Change Is Throwing off Key Timing for Wildflowers and Trees in Spring.” PBS NewsHour, 19 Mar. 2023, https://www.pbs.org/newshour/science/how-climate-change-is-throwing-off-key-timing-for-wildflowers-and-trees-in-spring.

Willis, Charles G., Brad R. Ruhfel, et al. “Favorable Climate Change Response Explains Non-Native Species’ Success in Thoreau’s Woods.” PLOS ONE, vol. 5, no. 1, Jan. 2010, p. e8878. PLoS Journals, https://doi.org/10.1371/journal.pone.0008878.

Willis, Charles G., Brad Ruhfel, et al. “Phylogenetic Patterns of Species Loss in Thoreau’s Woods Are Driven by Climate Change.” Proceedings of the National Academy of Sciences, vol. 105, no. 44, Nov. 2008, pp. 17029–33. pnas.org (Atypon), https://doi.org/10.1073/pnas.0806446105.

The Project:

Created by the Chicago Botanic Gardens; Budburst is a "community-focused, data-driven approach to plant conservation" platform that allows users to take photos natively in-app and upload photos from their camera roll to document the natural beauty of plants around them.

The app allows for 3 types of observation reports: phenology, pollinator, and milkweeds & monarchs. After answering a set of multiple choice questions with an option to include detail shots, the observations are added to an interactive map to allow users to see where they have previously made observations and easily return at a later date for continued observation throughout the plant's life cycle in order to help track the effects of climate change.

Participation Plan:

I incorporated my participation in this citizen science project into walks with my dog, Juniper. I was already taking her for walks around my community 3 times a day so all it took was taking an additional 10 seconds to take a picture of plants along our routes. I took pictures and completed reports on at least 3 different plants a day over the course of my participation in the project.

[bisham456]

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[mentorkart]

Learn Data Science through Data Science Course- MentorKart

Given today's massive amounts of data, data science is an essential component of many industries, and it is one of the most highly debated topics in IT circles. Its popularity has grown over time, and businesses have begun to use data science techniques to grow their businesses and increase customer satisfaction. In this blog, we'll define how to become a data scientist through a data science course.

What is Data Science?

Data science is the study of vast amounts of data using modern tools and techniques to discover previously unseen patterns, derive meaningful information, as well as make business decisions. To create predictive models, data scientists use the complex machine learning algorithms which they learn from a data science course.Data for analysis can be derived from a variety of sources and presented in a number of formats.

Data Science Lifecycle

Now that you understand what data science is, let us look at the data science lifecycle. The data science lifecycle is divided into five distinct stages, each with its own set of tasks:

Capture: Data acquisition,  signal reception, data entry, and data extraction are all captured. Collecting both structured and unstructured data is in this stage.

Maintain: Data Warehousing,  Data Staging,, Data Cleansing, Data Processing, and Data Architecture must all be maintained. Taking raw data and converting it into a usable format is in this stage.

Process: Data mining, clustering/classification, data modelling, and data summarization are all processes. Data scientists examine the prepared data for patterns, ranges, and biases to determine its usefulness in predictive analysis.

Analyse: Exploratory/Confirmatory, Regression, Predictive Analysis, Text Mining, Qualitative Analysis are all types of analyses. This is the meat of the life cycle. This stage entails running various analyses on the data.

Communicate: Data Reporting, Business Intelligence, Data Visualization, and Decision Making are all ways to communicate. Analysts prepare the analyses in easily readable forms like charts, graphs, and reports in this final step.

Data Science Course Prerequisites

Here are some technical terms you should know and will learn in a data science course.

The heart of Data Science is machine learning. Data Scientists must be well-versed in ML as well as have a basic understanding of statistics.

Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modelling is a subset of Machine Learning that entails determining which algorithm is best suited to solving a given problem and how to train these models.

Statistics are at the heart of data science. A firm grasp on statistics can assist you in extracting more intelligence and obtaining more meaningful results.

To carry out a successful data science project, some level of programming is required. Python and R are the most popular programming languages. Python is particularly popular due to its ease of use and support for numerous data science and machine learning libraries.

Databases An effective data scientist must understand how databases operate, how to manage them, and how to extract data from them.

Who is in charge of the data science process?

The people in charge of monitoring the data science training method are the business managers. Their main responsibility is to work with the data science team to characterise the problem and develop an analytical method. A data scientist may be in charge of the marketing, finance, or sales department and report to the department's executive. Their goal is to complete projects on time by working closely with data scientists and IT managers.

They are followed by the IT managers. The responsibilities will undoubtedly be more important if the member has been with the organisation for a long time. They are primarily in charge of creating the infrastructure and architecture that will enable data science activities. To ensure that data science teams operate efficiently and safely, they are constantly monitored and appropriately resourced. They may also be in charge of creating and maintaining data science teams' IT environments.

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[martinhealthactivist]

Laboratory Services Department Of Public Health & Setting

After the take a look at is completed, the outcome should be delivered in a timely style to the healthcare provider. Many take a look at methods use automated analyzers, thus minimizing opportunities for human error. In the past decade or so, integrated computer expertise has significantly enhanced the flexibility to accurately and persistently handle correct specimen identification, process of specimen testing, and take a look at end result reporting. In addition, these complicated instruments can incorporate surveillance methods to detect malfunctions or different discrepancies and bring them to the eye of the laboratory employees. Four indicators are mostly used to discover out the reliability of a medical laboratory check. Two of those, accuracy and precision, replicate how well the take a look at method performs day to day in a laboratory.

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Your healthcare supplier, medical workplace workers, the pattern collection website, or the laboratory performing the testing ought to talk about with you tips on how to put together for a check to avoid identified interferences. Data from medical checks are a half of the knowledge set that needs to be thought-about when a healthcare supplier makes a analysis. When a laboratory report indicates abnormal or unexpected outcomes, it's incumbent on your healthcare provider to further consider and corroborate the information laboratory services at hand to make sure an accurate analysis. If the data don't correspond with the clinical image, additional info could also be needed and retesting may be applicable. In some situations, the progression of the illness or condition may not be evident for the testing modality to be relevant. The study showed that LLS is extra relevant for PHCs with inhabitants catchment space lower than or equal to IPHS suggestions.

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[gtssidata4]

Scalable AI Data Pipeline In Government Sector

You'd like to be able to make a magic wand, and say "I would like ..." to find a solution capable of making informed choices and adapting to the latest information. Additionally, if the system could be taught by its own self, it would be a dream. It's like a fairytale, which it was to a point.

Artificial Intelligence has made enormous advancements in technology and scientific advancements across a variety of fields. It could dramatically alter the way that civilian and military activities are conducted. As of the present, only a few nations with massive military forces, including China, the United States, China and Russia are able to imagine having military supremacy.

While there is broad acceptance of the concept of AI is, which means the computers or digitally controlled robots capable of performing tasks that humans do, there are different opinions regarding how this can be accomplished. In a world where data science is becoming the new norm in the field of computer science and is generally accepted that it is intrinsically related to AI.

Five key points in the report:

Quality of data: The quality of the Speech Datasets needed for an application directly affects its the performance of the application, which is further impacted when considering AI on a large scale. Enterprise-grade AI projects usually deal with million of information points possibly in different formats and from various sources. Every data point needs to be identified with specific guidelines and serve various functions. If you reach the POC and pilot stages are completed and the number of experts and workflows on the data increase it becomes more important to ensure consistency.

Expertise and levels of skill: An AI project needs experts from various disciplines. The composition and the caliber of the team are crucial aspects when it comes to deciding to expand. The kinds of expertise needed to enhance the effectiveness of an AI process can be divided into technical knowledge as well as domain expertise and process expertise.

Edge Case Management Edge situations or rare instances in data are usually discovered in the last mile AI development life cycle. They are caused by the variety and complexity which exist in the actual world. They must be captured in the data in order to help train an AI model to recognize these and respond accordingly.

Governance and security of data: An investment in AI fundamentally requires an investment in a solid security infrastructure for information. In a time which remote work is becoming more popular, there is greater attention to make sure that the security of on-premises is maintained in the event that an employee works somewhere else.

Continuously training This process for AI creation is a constant one. As the world around us changes, AI products must adapt in line with the changing environment. The data being collected and utilized in AI is constantly changing, and data pipelines need to be designed to keep that in mind.

How can I make an image dataset?

The creation of a high-quality ML Dataset is a lengthy and challenging task. You need to take a methodical approach to gather information that will be used to construct an accurate and high-quality data set. First, you must determine the different sources of data which can be used to build the model. In the case of collecting video or image data for computer vision tasks there are a variety of choices.

Public Datasets

The most efficient solution is to utilize the public machine-learning dataset. They are readily available on the web, are open-source and available to anyone to download, use or share as well as modify. Be sure you check the data's license. If you intend to use the dataset in commercial machine-learning projects, a lot of public datasets require a subscription or license.

Copyleft licenses, particularly are risky when applied to commercial projects since they require that all derivative work (your model or your entire AI software) must be made available under the identical copyleft license. Certain datasets are specifically designed specifically for computer vision tasks such as object detection or facial recognition, as well as pose estimation. This means that they might not be appropriate to train AI models to solve different issue. It is required to develop an appropriate dataset for this scenario.

Custom Datasets

The information in custom datasets for Audio Transcripiton may be collected by using web scraping software cameras, cameras, and other sensors in order to build customized training sets that can be used that are machine-learning (mobile phones or CCTV video cameras webcams, etc.). Data collected for machine learning can be assisted by third-party provider of dataset services. If you do not have the time or the tools to make a high-quality dataset on your own this is a great alternative.

[jaanushree]

Data Science Course

Role of data analytics in the automotive industryPredictive analyticsHow does data science influence sustainability Initiatives?Using data analytics to manage growth and threat in the management of supply chains Jobs and careers in data analytics in the automotive industry Conclusion:The automotive industry has become more data-driven. Every day, vehicle sensors, GPS tracking, automated manufacturing processes, optimized inventory systems, and other devices generate a large amount of data. These data must be examined and optimized. Automotive companies can derive value from this data by extracting hidden information using predictive analytics solutions. Check out Learnbay for more information about data science courses and provide IBM certification and multiple live projects.

Data Analytics in Automotive Industry

The automotive industry has undergone a massive transformation in recent years, disrupting the traditional ecosystem of automotive players. Multiple advancements in the field of data analytics and its interrelationship with the automotive industry have led to smarter, more efficient, and more connected vehicles, resulting in a significant increase in sales and marketing.

As massive amounts of information are gathered and organized for use, big data analytics serves as the foundation for all other applications. Major use cases include switching the automotive industry, assisting with mechanization, and increasing automation. A data-driven approach is required for the production of safer and higher-quality vehicles. Data science can lead to better mobility solutions with more connected and self-driving vehicles.

Automobile revolutions such as electric and self-driving cars have completely transformed today's world. This significant advancement in the automotive industry would not have been possible without big data analytics. Many AI applications rely heavily on big data, emphasizing the importance of data analytics for automotive engineers.

Predictive analytics is widely used in the automotive industry to understand fundamental consumer purchase trends and make future predictions using techniques such as data mining/modeling, machine learning (ML), and artificial intelligence (AI). Checkout Learnbay data analytics course for detailed information on predictive analytics. 

All automotive manufacturers prioritize sustainability. Each automotive company has its own set of objectives when it comes to charging efficiency targets. Because each vehicle has its own target, data science is critical to optimizing the fuel efficiency of a company's entire line of vehicles. Automobile manufacturers who develop next-generation vehicles that push the envelope (such as designing fuel-efficient vehicles) can receive government credits for their efforts. This provides the most value to its customers while also being completely environmentally friendly and providing a potential source of income. 

Organizing customer information for data analysis is critical for any business, and the automotive industry is no exception. Consumers nowadays conduct extensive research before making a final decision to purchase any vehicle. It generates a massive amount of data that automakers can use to understand the competition and the trends that are driving the industry.

This data is generated from a variety of sources, making it increasingly more challenging to collect and analyze the available data. Using big data analytics, sales and marketing teams can identify and understand the force that has performed in the past.

Each stage of the automotive product life cycle is complicated by data science. More advanced analytics applications in the automotive industry on the application are for component suppliers to detect defective parts early in the manufacturing process. 

Big data can also assist the automotive industry in maintaining and making do with various insights such as previous vehicle purchases, online user behavior, and demographics to develop personalized marketing communications, including sharing relevant content. Companies can also use it to pinpoint potential locations for auto dealerships to maximize customer retention.

Anyone who follows the automotive industry's developments will recognize the warning signs and lessons. Companies in the automotive industry have no choice but to store and integrate big data analytics into their business processes. This includes upgrading their technology and allowing their IT systems to begin collecting and analyzing large amounts of data using machine learning and artificial intelligence.

As a result, they are forced to hire the best professionals for jobs in big data analytics, predictive analytics, AI, ML, and other technical fields. If you're interested in data analytics jobs and careers in the automotive industry, or any other industry, start learning technical skills and industry knowledge. You'll need a good combination of the two to persuade the recruiter during big data analytics job interviews.

[missmentelle]

Why Smart People Believe Stupid Things

If you’ve been paying attention for the last couple of years, you might have noticed that the world has a bit of a misinformation problem. 

The problem isn’t just with the recent election conspiracies, either. The last couple of years has brought us the rise (and occasionally fall) of misinformation-based movements like:

Sandy Hook conspiracies

Gamergate

Pizzagate

The MRA/incel/MGTOW movements

anti-vaxxers

flat-earthers

the birther movement

the Illuminati 

climate change denial

Spygate

Holocaust denial 

COVID-19 denial 

5G panic 

QAnon 

But why do people believe this stuff?

It would be easy - too easy - to say that people fall for this stuff because they’re stupid. We all want to believe that smart people like us are immune from being taken in by deranged conspiracies. But it’s just not that simple. People from all walks of life are going down these rabbit holes - people with degrees and professional careers and rich lives have fallen for these theories, leaving their loved ones baffled. Decades-long relationships have splintered this year, as the number of people flocking to these conspiracies out of nowhere reaches a fever pitch. 

So why do smart people start believing some incredibly stupid things? It’s because:

Our brains are built to identify patterns. 

Our brains fucking love puzzles and patterns. This is a well-known phenomenon called apophenia, and at one point, it was probably helpful for our survival - the prehistoric human who noticed patterns in things like animal migration, plant life cycles and the movement of the stars was probably a lot more likely to survive than the human who couldn’t figure out how to use natural clues to navigate or find food. 

The problem, though, is that we can’t really turn this off. Even when we’re presented with completely random data, we’ll see patterns. We see patterns in everything, even when there’s no pattern there. This is why people see Jesus in a burnt piece of toast or get superstitious about hockey playoffs or insist on always playing at a certain slot machine - our brains look for patterns in the constant barrage of random information in our daily lives, and insist that those patterns are really there, even when they’re completely imagined. 

A lot of conspiracy theories have their roots in people making connections between things that aren’t really connected. The belief that “vaccines cause autism” was bolstered by the fact that the first recognizable symptoms of autism happen to appear at roughly the same time that children receive one of their rounds of childhood immunizations - the two things are completely unconnected, but our brains have a hard time letting go of the pattern they see there. Likewise, many people were quick to latch on to the fact that early maps of COVID infections were extremely similar to maps of 5G coverage -  the fact that there’s a reasonable explanation for this (major cities are more likely to have both high COVID cases AND 5G networks) doesn’t change the fact that our brains just really, really want to see a connection there. 

Our brains love proportionality. 

Specifically, our brains like effects to be directly proportional to their causes - in other words, we like it when big events have big causes, and small causes only lead to small events. It’s uncomfortable for us when the reverse is true. And so anytime we feel like a “big” event (celebrity death, global pandemic, your precious child is diagnosed with autism) has a small or unsatisfying cause (car accident, pandemics just sort of happen every few decades, people just get autism sometimes), we sometimes feel the need to start looking around for the bigger, more sinister, “true” cause of that event. 

Consider, for instance, the attempted assassination of Pope John Paul II. In 1981, Pope John Paul II was shot four times by a Turkish member of a known Italian paramilitary secret society who’d recently escaped from prison - on the surface, it seems like the sort of thing conspiracy theorists salivate over, seeing how it was an actual multinational conspiracy. But they never had much interest in the assassination attempt. Why? Because the Pope didn’t die. He recovered from his injuries and went right back to Pope-ing. The event didn’t have a serious outcome, and so people are content with the idea that one extremist carried it out. The death of Princess Diana, however, has been fertile ground for conspiracy theories; even though a woman dying in a car accident is less weird than a man being shot four times by a paid political assassin, her death has attracted more conspiracy theories because it had a bigger outcome. A princess dying in a car accident doesn’t feel big enough. It’s unsatisfying. We want such a monumentous moment in history to have a bigger, more interesting cause. 

These theories prey on pre-existing fear and anger. 

Are you a terrified new parent who wants the best for their child and feels anxious about having them injected with a substance you don’t totally understand? Congrats, you’re a prime target for the anti-vaccine movement. Are you a young white male who doesn’t like seeing more and more games aimed at women and minorities, and is worried that “your” gaming culture is being stolen from you? You might have been very interested in something called Gamergate. Are you a right-wing white person who worries that “your” country and way of life is being stolen by immigrants, non-Christians and coastal liberals? You’re going to love the “all left-wingers are Satantic pedo baby-eaters” messaging of QAnon. 

Misinformation and conspiracy theories are often aimed strategically at the anxieties and fears that people are already experiencing. No one likes being told that their fears are insane or irrational; it’s not hard to see why people gravitate towards communities that say “yes, you were right all along, and everyone who told you that you were nuts to be worried about this is just a dumb sheep. We believe you, and we have evidence that you were right along, right here.” Fear is a powerful motivator, and you can make people believe and do some pretty extreme things if you just keep telling them “yes, that thing you’re afraid of is true, but also it’s way worse than you could have ever imagined.”

Real information is often complicated, hard to understand, and inherently unsatisfying. 

The information that comes from the scientific community is often very frustrating for a layperson; we want science to have hard-and-fast answers, but it doesn’t. The closest you get to a straight answer is often “it depends” or “we don’t know, but we think X might be likely”. Understanding the results of a scientific study with any confidence requires knowing about sampling practices, error types, effect sizes, confidence intervals and publishing biases. Even asking a simple question like “is X bad for my child” will usually get you a complicated, uncertain answer - in most cases, it really just depends. Not understanding complex topics makes people afraid - it makes it hard to trust that they’re being given the right information, and that they’re making the right choices. 

Conspiracy theories and misinformation, on the other hand, are often simple, and they are certain. Vaccines bad. Natural things good. 5G bad. Organic food good. The reason girls won’t date you isn’t a complex combination of your social skills, hygiene, appearance, projected values, personal circumstances, degree of extroversion, luck and life phase - girls won’t date you because feminism is bad, and if we got rid of feminism you’d have a girlfriend. The reason Donald Trump was an unpopular president wasn’t a complex combination of his public bigotry, lack of decorum, lack of qualifications, open incompetence, nepotism, corruption, loss of soft power, refusal to uphold the basic responsibilities of his position or his constant lying - they hated him because he was fighting a secret sex cult and they’re all in it. 

Instead of making you feel stupid because you’re overwhelmed with complex information, expert opinions and uncertain advice, conspiracy theories make you feel smart - smarter, in fact, than everyone who doesn’t believe in them. And that’s a powerful thing for people living in a credential-heavy world. 

Many conspiracy theories are unfalsifiable. 

It is very difficult to prove a negative. If I tell you, for instance, that there’s no such thing as a purple swan, it would be very difficult for me to actually prove that to you - I could spend the rest of my life photographing swans and looking for swans and talking to people who know a lot about swans, and yet the slim possibility would still exist that there was a purple swan out there somewhere that I just hadn’t found yet. That’s why, in most circumstances, the burden of proof lies with the person making the extraordinary claim - if you tell me that purple swans exist, we should continue to assume that they don’t until you actually produce a purple swan. 

Conspiracy theories, however, are built so that it’s nearly impossible to “prove” them wrong. Is there any proof that the world’s top-ranking politicians and celebrities are all in a giant child sex trafficking cult? No. But can you prove that they aren’t in a child sex-trafficking cult? No, not really. Even if I, again, spent the rest of my life investigating celebrities and following celebrities and talking to people who know celebrities, I still couldn’t definitely prove that this cult doesn’t exist - there’s always a chance that the specific celebrities I’ve investigated just aren’t in the cult (but other ones are!) or that they’re hiding evidence of the cult even better than we think. Lack of evidence for a conspiracy theory is always treated as more evidence for the theory - we can’t find anything because this goes even higher up than we think! They’re even more sophisticated at hiding this than we thought! People deeply entrenched in these theories don’t even realize that they are stuck in a circular loop where everything seems to prove their theory right - they just see a mountain of “evidence” for their side. 

Our brains are very attached to information that we “learned” by ourselves.

Learning accurate information is not a particularly interactive or exciting experience. An expert or reliable source just presents the information to you in its entirety, you read or watch the information, and that’s the end of it. You can look for more information or look for clarification of something, but it’s a one-way street - the information is just laid out for you, you take what you need, end of story. 

Conspiracy theories, on the other hand, almost never show their hand all at once. They drop little breadcrumbs of information that slowly lead you where they want you to go. This is why conspiracy theorists are forever telling you to “do your research” - they know that if they tell you everything at once, you won’t believe them. Instead, they want you to indoctrinate yourself slowly over time, by taking the little hints they give you and running off to find or invent evidence that matches that clue. If I tell you that celebrities often wear symbols that identify them as part of a cult and that you should “do your research” about it, you can absolutely find evidence that substantiates my claim - there are literally millions of photos of celebrities out there, and anyone who looks hard enough is guaranteed to find common shapes, poses and themes that might just mean something (they don’t - eyes and triangles are incredibly common design elements, and if I took enough pictures of you, I could also “prove” that you also clearly display symbols that signal you’re in the cult). 

The fact that you “found” the evidence on your own, however, makes it more meaningful to you. We trust ourselves, and we trust that the patterns we uncover by ourselves are true. It doesn’t feel like you’re being fed misinformation - it feels like you’ve discovered an important truth that “they” didn’t want you to find, and you’ll hang onto that for dear life. 

Older people have not learned to be media-literate in a digital world. 

Fifty years ago, not just anyone could access popular media. All of this stuff had a huge barrier to entry - if you wanted to be on TV or be in the papers or have a radio show, you had to be a professional affiliated with a major media brand. Consumers didn’t have easy access to niche communities or alternative information - your sources of information were basically your local paper, the nightly news, and your morning radio show, and they all more or less agreed on the same set of facts. For decades, if it looked official and it appeared in print, you could probably trust that it was true. 

Of course, we live in a very different world today - today, any asshole can accumulate an audience of millions, even if they have no credentials and nothing they say is actually true (like “The Food Babe”, a blogger with no credentials in medicine, nutrition, health sciences, biology or chemistry who peddles health misinformation to the 3 million people who visit her blog every month). It’s very tough for older people (and some younger people) to get their heads around the fact that it’s very easy to create an “official-looking” news source, and that they can’t necessarily trust everything they find on the internet. When you combine that with a tendency toward “clickbait headlines” that often misrepresent the information in the article, you have a generation struggling to determine who they can trust in a media landscape that doesn’t at all resemble the media landscape they once knew. 

These beliefs become a part of someone’s identity. 

A person doesn’t tell you that they believe in anti-vaxx information - they tell you that they ARE an anti-vaxxer. Likewise, people will tell you that they ARE a flat-earther, a birther, or a Gamergater. By design, these beliefs are not meant to be something you have a casual relationship with, like your opinion of pizza toppings or how much you trust local weather forecasts - they are meant to form a core part of your identity. 

And once something becomes a core part of your identity, trying to make you stop believing it becomes almost impossible. Once we’ve formed an initial impression of something, facts just don’t change our minds. If you identify as an antivaxxer and I present evidence that disproves your beliefs, in your mind, I’m not correcting inaccurate information - I am launching a very personal attack against a core part of who you are. In fact, the more evidence I present, the more you will burrow down into your antivaxx beliefs, more confident than ever that you are right. Admitting that you are wrong about something that is important to you is painful, and your brain would prefer to simply deflect conflicting information rather than subject you to that pain.

We can see this at work with something called the confirmation bias. Simply put, once we believe something, our brains hold on to all evidence that that belief is true, and ignore evidence that it’s false. If I show you 100 articles that disprove your pet theory and 3 articles that confirm it, you’ll cling to those 3 articles and forget about the rest. Even if I show you nothing but articles that disprove your theory, you’ll likely go through them and pick out any ambiguous or conflicting information as evidence for “your side”, even if the conclusion of the article shows that you are wrong - our brains simply care about feeling right more than they care about what is actually true.  

There is a strong community aspect to these theories. 

There is no one quite as supportive or as understanding as a conspiracy theorist - provided, of course, that you believe in the same conspiracy theories that they do. People who start looking into these conspiracy theories are told that they aren’t crazy, and that their fears are totally valid. They’re told that the people in their lives who doubted them were just brainwashed sheep, but that they’ve finally found a community of people who get where they’re coming from. Whenever they report back to the group with the “evidence” they’ve found or the new elaborations on the conspiracy theory that they’ve been thinking of (“what if it’s even worse than we thought??”), they are given praise for their valuable contributions. These conspiracy groups often become important parts of people’s social networks - they can spend hours every day talking with like-minded people from these communities and sharing their ideas. 

Of course, the flipside of this is that anyone who starts to doubt or move away from the conspiracy immediately loses that community and social support. People who have broken away from antivaxx and QAnon often say that the hardest part of leaving was losing the community and friendships they’d built - not necessarily giving up on the theory itself. Many people are rejected by their real-life friends and family once they start to get entrenched in conspiracy theories; the friendships they build online in the course of researching these theories often become the only social supports they have left, and losing those supports means having no one to turn to at all. This is by design - the threat of losing your community has kept people trapped in abusive religious sects and cults for as long as those things have existed. 

[generation1point5]

Blockchain, Finance, and the Videogame Industry

NFTs being sold to corporate executives in the gaming sphere is the symptom of the underlying problem of the industry as a whole as developed under a capitalist organization of the economy. If there was ever a torpedo to sink the notion that videogames in the AAA market are not a mere profit vehicle for finance bros, NFTs are it.

Understandably, a multimillion-dollar project is a significant risk, that all-important trigger word so often thrown around by economics majors. But videogames at its heart ought to trend more towards art, not finance. This is why most the pixels and apes of the cryptobros are soulless, without regard to meaning or any value outside of a psychotic mindset that is symptomatic of capitalist brainrot. It is the greatest example of how money itself is a social construct, made into an unspoken contract that has dictated the fates and suffering of untold billions. Art worthy of the semantic underpinnings the word carries is diametrically opposed to the principles of financial growth. Art is not mere imitation, but interpretation. Even attempts at realism acknowledges a bias towards the five senses over the unseen. This is true of videogames as much as it is other art forms.

In this respect, indie titles hold a virtual monopoly in artistic integrity, but that is not quite the end of the story. There are many artists, but only one Michelangelo, only one Picasso. Likewise, the success and recognition of indie titles are also limited; of the countless indie games greenlit by Steam, only a few emerge out of the obscurity of the mass collective; there is much more to the value of art than integrity or recognition, but that is beyond the scope of my current musings.

The overall creative process for art is inherently risky in a capitalist model of the economy. Success, whether in monetary or even in terms of simple recognition, is never guaranteed, and a big reason (I think) why so much art is often left unfinished and incomplete to begin with. It would be perceived to be a wasted effort to complete a project that was never going to achieve the goals set upon it, and in a capitalist model that is compounded by obligatory financial rewards not necessarily built into the model, but necessary when outside funding and stakeholders are involved. Because economics claims to be a science rather than an art, it has no tool beyond data gathering and analysis that often leaves a stilted, disjointed, and incomplete picture by which to guide developers in the creative process. Risk pressures artists from being able to fill the crucial gaps that are left void and empty.

Gone are the days when a bold vision could be met with the enthusiasm and the support of publishers to create a truly innovative thesis in the development of videogames. Homeworld, the first true 3D RTS and still one of the only few of its kind, was built on the trust of a one million dollar, one year cycle that had to grow to three years and three million dollars in investment, and yet the product of that risk was a true masterpiece that still stands out in an era where travels in space has become more vivid and life-like than ever before. Those days are firmly rested in the first decade of the new millennium.

Crowdfunding has emerged as something of a grassroots alternative, but that has come with its own share of controversies as aspirational visions have proven to become more marketing than actual substance (Star Citizen being chief example of this), and is neither a perfect solution for either producer or consumer. It still relies on the same capitalistic practices and vices that dictate the toxic relationship between the two that more often ends with dissatisfaction than a true synthesis of those looking to enjoy art and the means of supporting those who further it.

In this respect, mass workplace unionization stands as a promising alternative, alongside cooperatives that can establish healthy work-life balance among its constituents. A social organization of videogames developers, whether large or small, would encourage artistic buy-in from everyone involved with development, while also ensuring job security, a living wage and adequate medical coverage, among a myriad of other benefits. While not an all-encompassing solution to some of the notable cultural issues that have been rooted in the history of the art, it would nevertheless be a strong step forwards in creating a healthy environment that would allow videogames as an art form to flourish as it did in its heyday. That is not just my sentimentality talking.

Of course, this can all just be thought also as an argument for global socialization in general. Capitalism makes monsters of all its success stories and crushes anything that it doesn’t consider such.