#Career in artificial intelligence

[khushidubeyblog]

PGDM Specialization in AI & ML: Preparing for the Future of Business and Technology

[diptisinghblog]

Artificial Intelligence and Machine Learning Courses in Bangalore: A Guide to Advancing Your Career

[sauravisubbha00]

Career in Artificial Intelligence

AI encompasses various subfields, including machine learning, natural language processing, computer vision, robotics, expert systems, and neural networks. These techniques and technologies enable machines to learn from data, recognize patterns, understand human language, interpret images and videos, and interact with the physical world.

[artificialintelligence0]

Top 10 Artificial intelligence courses in Delhi

There is a high demand for skilled professionals who can develop, implement, and manage AI systems. Below are some career paths within artificial intelligence:

Machine Learning Engineer

Data Scientist

AI Researcher

AI Ethicist

Natural Language Processing (NLP) Engineer

Robotics Engineer

AI Product Manager and many more

Due to more and more career opportunities in this field, people are interested in taking an artificial intelligence course.

[naya-mishra]

here, we will discuss the scope of AI in India in various sectors such as banking, healthcare, cybersecurity, and education is enormous, and its potential to transform these sectors is significant.

[mbti-notes]

Anon wrote: hello! thank you for running this blog. i hope your vacation was well-spent!

i am an enfp in the third year of my engineering degree. i had initially wanted to do literature and become an author. however, due to the job security associated with this field, my parents got me to do computer science, specialising in artificial intelligence. i did think it was the end of my life at the time, but eventually convinced myself otherwise. after all, i could still continue reading and writing as hobbies.

now, three years in, i am having the same thoughts again. i've been feeling disillusioned from the whole gen-ai thing due to art theft issues and people using it to bypass - dare i say, outsource - creative work. also, the environmental impact of this technology is astounding. yet, every instructor tells us to use ai to get information that could easily be looked up in textbooks or google. what makes it worse is that i recently lost an essay competition to a guy who i know for a fact used chatgpt.

i can't help feeling that by working in this industry, i am becoming a part of the problem. at the same time, i feel like a conservative old person who is rejecting modern technology and griping about 'the good old days'.

another thing is that college work is just so all-consuming and tiring that i've barely read or written anything non-academic in the past few years. quitting my job and becoming a writer a few years down the road is seeming more and more like a doomed possibility.

i've been trying to do what i can at my level. i write articles about ethical considerations in ai for the college newsletter. i am in a technical events club, and am planning out an artificial intelligence introductory workshop for juniors where i will include these topics, if approved by the superiors.

from what i've read on your blog, it doesn't seem like you have a very high opinion of ai, either, but i've only seen you address it in terms of writing. i'd like to know, are there any ai applications that you find beneficial? i think that now that i am here, i could try to make a difference by working on projects that actually help people, rather than use some chatgpt api to do the same things, repackaged. i just felt like i need the perspective of someone who thinks differently than all those around me. not in a 'feed my tunnel-vision' way, but in a 'tell me i'm not stupid' way.

----------------------

It's kind of interesting (in the "isn't life whacky?" sort of way) you chose the one field that has the potential to decimate the field that you actually wanted to be in. I certainly understand your inner conflict and I'll give you my personal views, but I don't know how much they will help your decision making.

I'm of course concerned about the ramifications on writing not just because I'm a writer but because, from the perspective of education and personal growth, I understand the enormous value of writing skills. Learning to write analytically is challenging. I've witnessed many people meet that challenge bravely, and in the process, they became much more intelligent and thoughtful human beings, better able to contribute positively to society. So, it pains me to see the attitude of "don't have to learn it cuz the machine does it". However, writing doesn't encompass my full view on AI.

I wouldn't necessarily stereotype people who are against new technology as "old and conservative", though some of them are. My parents taught me to be an early adopter of new tech, but it doesn't mean I don't have reservations about it. I think, psychologically, the main reason people resist is because of the real threat it poses. Historically, we like to gloss over the real human suffering that results from technological advancement. But it is a reasonable and legitimate response to resist something that threatens your livelihood and even your very existence.

For example, it is already difficult enough to make a living in the arts, and AI just might make it impossible. Even if you do come up with something genuinely creative and valuable, how are you going to make a living with it? As soon as creative products are digitized, they just get scraped up, regurgitated, and disseminated to the masses with no credit or compensation given to the original creator. It's cannibalism. Cannibalism isn't sustainable.

I wonder if people can seriously imagine a society where human creativity in the arts has been made obsolete and people only have exposure to AI creation. There are plenty of people who don't fully grasp the value of human creativity, so they wouldn't mind it, but I would personally consider it to be a kind of hell.

I occasionally mention that my true passion is researching "meaning" and how people come to imbue their life with a sense of meaning. Creativity has a major role to play in 1) almost everything that makes life/living feel worthwhile, 2) generating a culture that is worth honoring and preserving, and 3) building a society that is worthy of devoting our efforts to.

Living in a capitalist society that treats people as mere tools of productivity and treats education as a mere means to a paycheck already robs us of so much meaning. In many ways, AI is a logical result of that mindset, of trying to "extract" whatever value humans have left to offer, until we are nothing but empty shells.

I don't think it's a coincidence that AI comes out of a society that devalues humanity to the point where a troubling portion of the population suffers marginalization, mental disorder, and/or feels existentially empty. Many of the arguments I've heard from AI proponents about how it can improve life sound to me like they're actually going to accelerate spiritual starvation.

Existential concerns are serious enough, before we even get to the environmental concerns. For me, environment is the biggest reason to be suspicious of AI and its true cost. I think too many people are unaware of the environmental impact of computing and networking in general, let alone running AI systems. I recently read about how much energy it takes to store all the forgotten chats, memes, and posts on social media. AI ramps up carbon emissions dramatically and wastes an already dwindling supply of fresh water.

Can we really afford a mass experiment with AI at a time when we are already hurtling toward climate catastrophe? When you think about how much AI is used for trivial entertainment or pointless busywork, it doesn't seem worth the environmental cost. I care about this enough that I try to reduce my digital footprint. But I'm just one person and most of the population is trending the other way.

With respect to integrating AI into personal life or everyday living, I struggle to see the value, often because those who might benefit the most are the ones who don't have access. Yes, I've seen some people have success with using AI to plan and organize, but I also always secretly wonder at how their life got to the point of needing that much outside help. Sure, AI may help with certain disadvantages such as learning or physical disabilities, but this segment of the population is usually the last to reap the benefits of technology.

More often than not, I see people using AI to lie, cheat, steal, and protect their own privilege. It's particularly sad for me to see people lying to themselves, e.g., believing that they're smart for using AI when they're actually making themselves stupider, or thinking that an AI companion can replace real human relationship.

I continue to believe that releasing AI into the wild, without developing proper safeguards, was the biggest mistake made so far. The revolts at OpenAI prove, once again, that companies cannot be trusted to regulate themselves. Tech companies need a constant stream of data to feed the beast and they're willing to sacrifice our well-being to do it. It seems the only thing we can do as individuals is stop offering up our data, but that's not going to happen en masse.

Even though you're aware of these issues, I want to mention them for those who aren't, and for the sake of emphasizing just how important it is to regulate AI and limit its use to the things that are most likely to produce a benefit to humanity, in terms of actually improving quality of human life in concrete terms.

In my opinion, the most worthwhile place to use AI is medicine and medical research. For example, aggregating and analyzing information for doctors, assisting surgeons with difficult procedures, and coming up with new possibilities for vaccines, treatments, and cures is where I'd like to see AI shine. I'd also love to see AI applied to:

scientific research, to help scientists sort, manage, and process huge amounts of information

educational resources, to help learners find quality information more efficiently, rather than feeding them misinformation

engineering and design, to build more sustainable infrastructure

space exploration, to find better ways of traveling through space or surviving on other planets

statistical analysis, to help policymakers take a more objective look at whether solutions are actually working as intended, as opposed to being blinded by wishful thinking, bias, hubris, or ideology (I recognize this point is controversial since AI can be biased as well)

Even though you work in the field, you're still only one person, so you don't have that much more power than anyone else to change its direction. There's no putting the worms back in the can at this point. I agree with you that, for the sake of your well-being, staying in the field means choosing your work carefully. However, if you want to work for an organization that doesn't sacrifice people at the altar of profit, it might be slim pickings and the pay might not be great. Staying true to your values can be costly too.

[savagechickens]

Career Fair.

And more careers.

[tech-insides]

What are the skills needed for a data scientist job?

It’s one of those careers that’s been getting a lot of buzz lately, and for good reason. But what exactly do you need to become a data scientist? Let’s break it down.

Technical Skills

First off, let's talk about the technical skills. These are the nuts and bolts of what you'll be doing every day.

Programming Skills: At the top of the list is programming. You’ll need to be proficient in languages like Python and R. These are the go-to tools for data manipulation, analysis, and visualization. If you’re comfortable writing scripts and solving problems with code, you’re on the right track.

Statistical Knowledge: Next up, you’ve got to have a solid grasp of statistics. This isn’t just about knowing the theory; it’s about applying statistical techniques to real-world data. You’ll need to understand concepts like regression, hypothesis testing, and probability.

Machine Learning: Machine learning is another biggie. You should know how to build and deploy machine learning models. This includes everything from simple linear regressions to complex neural networks. Familiarity with libraries like scikit-learn, TensorFlow, and PyTorch will be a huge plus.

Data Wrangling: Data isn’t always clean and tidy when you get it. Often, it’s messy and requires a lot of preprocessing. Skills in data wrangling, which means cleaning and organizing data, are essential. Tools like Pandas in Python can help a lot here.

Data Visualization: Being able to visualize data is key. It’s not enough to just analyze data; you need to present it in a way that makes sense to others. Tools like Matplotlib, Seaborn, and Tableau can help you create clear and compelling visuals.

Analytical Skills

Now, let’s talk about the analytical skills. These are just as important as the technical skills, if not more so.

Problem-Solving: At its core, data science is about solving problems. You need to be curious and have a knack for figuring out why something isn’t working and how to fix it. This means thinking critically and logically.

Domain Knowledge: Understanding the industry you’re working in is crucial. Whether it’s healthcare, finance, marketing, or any other field, knowing the specifics of the industry will help you make better decisions and provide more valuable insights.

Communication Skills: You might be working with complex data, but if you can’t explain your findings to others, it’s all for nothing. Being able to communicate clearly and effectively with both technical and non-technical stakeholders is a must.

Soft Skills

Don’t underestimate the importance of soft skills. These might not be as obvious, but they’re just as critical.

Collaboration: Data scientists often work in teams, so being able to collaborate with others is essential. This means being open to feedback, sharing your ideas, and working well with colleagues from different backgrounds.

Time Management: You’ll likely be juggling multiple projects at once, so good time management skills are crucial. Knowing how to prioritize tasks and manage your time effectively can make a big difference.

Adaptability: The field of data science is always evolving. New tools, techniques, and technologies are constantly emerging. Being adaptable and willing to learn new things is key to staying current and relevant in the field.

Conclusion

So, there you have it. Becoming a data scientist requires a mix of technical prowess, analytical thinking, and soft skills. It’s a challenging but incredibly rewarding career path. If you’re passionate about data and love solving problems, it might just be the perfect fit for you.

Good luck to all of you aspiring data scientists out there!

[ismailfazil1-blog]

The Human Brain vs. Supercomputers: The Ultimate Comparison

Are Supercomputers Smarter Than the Human Brain?

This article delves into the intricacies of this comparison, examining the capabilities, strengths, and limitations of both the human brain and supercomputers.

[jcmarchi]

The Importance of Investing in Soft Skills in the Age of AI

New Post has been published on https://thedigitalinsider.com/the-importance-of-investing-in-soft-skills-in-the-age-of-ai/

The Importance of Investing in Soft Skills in the Age of AI

I’ll set out my stall and let you know I am still an AI skeptic. Heck, I still wrap “AI” in quotes a lot of the time I talk about it. I am, however, skeptical of the present, rather than the future. I wouldn’t say I’m positive or even excited about where AI is going, but there’s an inevitability that in development circles, it will be further engrained in our work.

We joke in the industry that the suggestions that AI gives us are more often than not, terrible, but that will only improve in time. A good basis for that theory is how fast generative AI has improved with image and video generation. Sure, generated images still have that “shrink-wrapped” look about them, and generated images of people have extra… um… limbs, but consider how much generated AI images have improved, even in the last 12 months.

There’s also the case that VC money is seemingly exclusively being invested in AI, industry-wide. Pair that with a continuously turbulent tech recruitment situation, with endless major layoffs and even a skeptic like myself can see the writing on the wall with how our jobs as developers are going to be affected.

The biggest risk factor I can foresee is that if your sole responsibility is to write code, your job is almost certainly at risk. I don’t think this is an imminent risk in a lot of cases, but as generative AI improves its code output — just like it has for images and video — it’s only a matter of time before it becomes a redundancy risk for actual human developers.

Do I think this is right? Absolutely not. Do I think it’s time to panic? Not yet, but I do see a lot of value in evolving your skillset beyond writing code. I especially see the value in improving your soft skills.

What are soft skills?

A good way to think of soft skills is that they are life skills. Soft skills include:

communicating with others,

organizing yourself and others,

making decisions, and

adapting to difficult situations.

I believe so much in soft skills that I call them core skills and for the rest of this article, I’ll refer to them as core skills, to underline their importance.

The path to becoming a truly great developer is down to more than just coding. It comes down to how you approach everything else, like communication, giving and receiving feedback, finding a pragmatic solution, planning — and even thinking like a web developer.

I’ve been working with CSS for over 15 years at this point and a lot has changed in its capabilities. What hasn’t changed though, is the core skills — often called “soft skills” — that are required to push you to the next level. I’ve spent a large chunk of those 15 years as a consultant, helping organizations — both global corporations and small startups — write better CSS. In almost every single case, an improvement of the organization’s core skills was the overarching difference.

The main reason for this is a lot of the time, the organizations I worked with coded themselves into a corner. They’d done that because they just plowed through — Jira ticket after Jira ticket — rather than step back and question, “is our approach actually working?” By focusing on their team’s core skills, we were often — and very quickly — able to identify problem areas and come up with pragmatic solutions that were almost never development solutions. These solutions were instead:

Improving communication and collaboration between design and development teams

Reducing design “hand-off” and instead, making the web-based output the source of truth

Moving slowly and methodically to move fast

Putting a sharp focus on planning and collaboration between developers and designers, way in advance of production work being started

Changing the mindset of “plow on” to taking a step back, thoroughly evaluating the problem, and then developing a collaborative and by proxy, much simpler solution

Will improving my core skills actually help?

One thing AI cannot do — and (hopefully) never will be able to do — is be human. Core skills — especially communication skills — are very difficult for AI to recreate well because the way we communicate is uniquely human.

I’ve been doing this job a long time and something that’s certainly propelled my career is the fact I’ve always been versatile. Having a multifaceted skillset — like in my case, learning CSS and HTML to improve my design work — will only benefit you. It opens up other opportunities for you too, which is especially important with the way the tech industry currently is.

If you’re wondering how to get started on improving your core skills, I’ve got you. I produced a course called Complete CSS this year but it’s a slight rug-pull because it’s actually a core skills course that uses CSS as a context. You get to learn some iron-clad CSS skills alongside those core skills too, as a bonus. It’s definitely worth checking out if you are interested in developing your core skills, especially so if you receive a training budget from your employer.

Wrapping up

The main message I want to get across is developing your core skills is as important — if not more important — than keeping up to date with the latest CSS or JavaScript thing. It might be uncomfortable for you to do that, but trust me, being able to stand yourself out over AI is only going to be a good thing, and improving your core skills is a sure-fire way to do exactly that.

[mdanwarhussain]

https://n9.cl/065sm

[ogxfuturetech]

The Comprehensive Guide to Web Development, Data Management, and More 

Introduction 

Everything today is technology driven in this digital world. There's a lot happening behind the scenes when you use your favorite apps, go to websites, and do other things with all of those zeroes and ones — or binary data. In this blog, I will be explaining what all these terminologies really means and other basics of web development, data management etc. We will be discussing them in the simplest way so that this becomes easy to understand for beginners or people who are even remotely interested about technology.  JOIN US

What is Web Development? 

Web development refers to the work and process of developing a website or web application that can run in a web browser. From laying out individual web page designs before we ever start coding, to how the layout will be implemented through HTML/CSS. There are two major fields of web development — front-end and back-end. 

Front-End Development 

Front-end development, also known as client-side development, is the part of web development that deals with what users see and interact with on their screens. It involves using languages like HTML, CSS, and JavaScript to create the visual elements of a website, such as buttons, forms, and images. JOIN US

HTML (HyperText Markup Language): 

HTML is the foundation of all website, it helps one to organize their content on web platform. It provides the default style to basic elements such as headings, paragraphs and links. 

CSS (Cascading Style Sheets):  

styles and formats HTML elements. It makes an attractive and user-friendly look of webpage as it controls the colors, fonts, layout. 

JavaScript :  

A language for adding interactivity to a website Users interact with items, like clicking a button to send in a form or viewing images within the slideshow. JOIN US

Back-End Development 

The difference while front-end development is all about what the user sees, back end involves everything that happens behind. The back-end consists of a server, database and application logic that runs on the web. 

Server: 

A server is a computer that holds website files and provides them to the user browser when they request it. Server-Side: These are populated by back-end developers who build and maintain servers using languages like Python, PHP or Ruby. 

Database:  

The place where a website keeps its data, from user details to content and settings The database is maintained with services like MySQL, PostgreSQL, or MongoDB. JOIN US

Application Logic —  

the code that links front-end and back-end It takes user input, gets data from the database and returns right informations to front-end area. 

Why Proper Data Management is Absolutely Critical 

Data management — Besides web development this is the most important a part of our Digital World. What Is Data Management? It includes practices, policies and procedures that are used to collect store secure data in controlled way. 

Data Storage –  

data after being collected needs to be stored securely such data can be stored in relational databases or cloud storage solutions. The most important aspect here is that the data should never be accessed by an unauthorized source or breached. JOIN US

Data processing:  

Right from storing the data, with Big Data you further move on to process it in order to make sense out of hordes of raw information. This includes cleansing the data (removing errors or redundancies), finding patterns among it, and producing ideas that could be useful for decision-making. 

Data Security:  

Another important part of data management is the security of it. It refers to defending data against unauthorized access, breaches or other potential vulnerabilities. You can do this with some basic security methods, mostly encryption and access controls as well as regular auditing of your systems. 

Other Critical Tech Landmarks 

There are a lot of disciplines in the tech world that go beyond web development and data management. Here are a few of them: 

Cloud Computing 

Leading by example, AWS had established cloud computing as the on-demand delivery of IT resources and applications via web services/Internet over a decade considering all layers to make it easy from servers up to top most layer. This will enable organizations to consume technology resources in the form of pay-as-you-go model without having to purchase, own and feed that infrastructure. JOIN US

Cloud Computing Advantages:  

Main advantages are cost savings, scalability, flexibility and disaster recovery. Resources can be scaled based on usage, which means companies only pay for what they are using and have the data backed up in case of an emergency. 

Examples of Cloud Services: 

Few popular cloud services are Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. These provide a plethora of services that helps to Develop and Manage App, Store Data etc. 

Cybersecurity 

As the world continues to rely more heavily on digital technologies, cybersecurity has never been a bigger issue. Protecting computer systems, networks and data from cyber attacks is called Cyber security. 

Phishing attacks, Malware, Ransomware and Data breaches: 

This is common cybersecurity threats. These threats can bear substantial ramifications, from financial damages to reputation harm for any corporation. 

Cybersecurity Best Practices:  

In order to safeguard against cybersecurity threats, it is necessary to follow best-practices including using strong passwords and two-factor authorization, updating software as required, training employees on security risks. 

Artificial Intelligence and Machine Learning 

Artificial Intelligence (AI) and Machine Learning (ML) represent the fastest-growing fields of creating systems that learn from data, identifying patterns in them. These are applied to several use-cases like self driving cars, personalization in Netflix. 

AI vs ML —  

AI is the broader concept of machines being able to carry out tasks in a way we would consider “smart”. Machine learning is a type of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. JOIN US

Applications of Artificial Intelligence and Machine Learning: some common applications include Image recognition, Speech to text, Natural language processing, Predictive analytics Robotics. 

Web Development meets Data Management etc. 

We need so many things like web development, data management and cloud computing plus cybersecurity etc.. but some of them are most important aspects i.e. AI/ML yet more fascinating is where these fields converge or play off each other. 

Web Development and Data Management 

Web Development and Data Management goes hand in hand. The large number of websites and web-based applications in the world generate enormous amounts of data — from user interactions, to transaction records. Being able to manage this data is key in providing a fantastic user experience and enabling you to make decisions based on the right kind of information. 

E.g. E-commerce Website, products data need to be saved on server also customers data should save in a database loosely coupled with orders and payments. This data is necessary for customization of the shopping experience as well as inventory management and fraud prevention. 

Cloud Computing and Web Development 

The development of the web has been revolutionized by cloud computing which gives developers a way to allocate, deploy and scale applications more or less without service friction. Developers now can host applications and data in cloud services instead of investing for physical servers. 

E.g. A start-up company can use cloud services to roll out the web application globally in order for all users worldwide could browse it without waiting due unavailability of geolocation prohibited access. 

The Future of Cybersecurity and Data Management 

Which makes Cybersecurity a very important part of the Data management. The more data collected and stored by an organization, the greater a target it becomes for cyber threats. It is important to secure this data using robust cybersecurity measures, so that sensitive information remains intact and customer trust does not weaken. JOIN US

Ex: A healthcare provider would have to protect patient data in order to be compliant with regulations such as HIPAA (Health Insurance Portability and Accountability Act) that is also responsible for ensuring a degree of confidentiality between a provider and their patients. 

Conclusion 

Well, in a nutshell web-developer or Data manager etc are some of the integral parts for digital world.

As a Business Owner, Tech Enthusiast or even if you are just planning to make your Career in tech — it is important that you understand these. With the progress of technology never slowing down, these intersections are perhaps only going to come together more strongly and develop into cornerstones that define how we live in a digital world tomorrow. 

With the fundamental knowledge of web development, data management, automation and ML you will manage to catch up with digital movements. Whether you have a site to build, ideas data to manage or simply interested in what’s hot these days, skills and knowledge around the above will stand good for changing tech world. JOIN US

[artificialintelligence0]

Top 10 Artificial intelligence courses in Delhi

AI has revolutionized every aspect of life and work, from healthcare to banking, manufacturing to entertainment. As a result, the demand for skilled AI professionals has increased dramatically, creating an excellent opportunity for individuals aspiring to excel in this rapidly evolving field.

[artificialintelligenceai01]

Artificial intelligence course in Pitampura

AI enables machines to think and act like human beings, but more quickly and with more processing power. Numerous industries, including finance, healthcare, national security, transportation, and smart cities, can benefit from the use of AI technology. It can streamline processes, improve goods and services, speed up analysis, improve accuracy, and maximize the value of data.

Since Jeetech Academy in Pitampura, Delhi offers one of the best artificial intelligence courses in Delhi, you should check them out.