#artificial intelligence b tech course

[allianceuniversityeducation]

B Tech in Artificial Intelligence: Scope & Colleges

AI is the future—start your journey now. Get into a leading B Tech artificial intelligence program. Master data science, ML, and neural networks. Explore India’s best AI colleges today.

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[engineeringpu]

Student Journey – Vijeta’s STEM Story

At Plaksha University, we believe that talent knows no boundaries. Meet Vijeta Raghuvanshi – a third-year CSAI (Computer Science & Artificial Intelligence) major at Plaksha.

[woxsennuniversity]

Woxsen University is recognized as one of the top B.Tech colleges in India, offering cutting-edge programs in engineering and technology. Its B.Tech curriculum is designed to equip students with technical expertise and problem-solving skills through a blend of theoretical knowledge and practical application. Woxsen provides specializations in Artificial Intelligence, Data Science, and other emerging fields, ensuring that graduates are industry-ready. The university’s state-of-the-art labs, experienced faculty, and industry partnerships create a learning environment that fosters innovation and career success.

[mahindrauniversity]

Mahindra University offers a B.Tech in Artificial Intelligence and a comprehensive curriculum combining foundational knowledge and practical applications. The program focuses on machine learning, data science, and ethics, ensuring students gain hands-on experience and interdisciplinary skills. This prepares graduates for various careers in the dynamic field of AI. Visit Our Official Website To Learn More.

[gitengineering]

 Best engineering college in Kerala| The Expansive Scope of Computer Engineering in India

Explore vast career prospects in computer engineering at GIT Engineering College, a top-ranked institution in Kottayam. Specialize in artificial intelligence, software development, data science, and more. Start your journey towards a tech-driven future today!

Computer engineering offers immense scope and opportunities, making it a popular choice among students. Are you curious about the possibilities in this dynamic field? Let’s delve into what computer engineering entails and the vast career prospects it offers.

 What is Computer Engineering?

Computer Engineering is a branch of engineering focused on developing and evolving both computer hardware and software. This field integrates software development and design, hardware-software integration, and electronic management of computer systems. It encompasses circuit designing, computing, design, and control of microcontrollers, microprocessors, PCs, supercomputers, and more.

 Scope of Computer Engineering

The rapid growth of technology and the IT sector has significantly increased the scope of computer engineering in India. The digital transformation spurred by the pandemic has further accelerated this trend, expanding the IT sector from a small flower garden to a vast field. A B.Tech in Computer Science equips students with foundational knowledge in systems, computer architecture, networking, database systems, operating systems, programming, website design, computation, e-commerce, software and hardware studies, and multimedia applications.

Graduates with a B.Tech in Computer Science can earn between ten to thirty lakhs per annum. They can seek employment in various sectors, including MNCs, NGOs, private firms like Wipro, Google, Infosys, HCL, Facebook, Microsoft, Amazon, Flipkart, IBM, Adobe, and government organizations through the GATE examination. Computer engineers find opportunities in software companies, banks, public sectors, colleges and universities, IT firms, media and entertainment, PR, advertising, finance, research labs, medical fields, digital consultancy, armed forces, and more.

 Career Domains in Computer Engineering

Computer engineering offers careers in:

- Artificial Intelligence and Robotics

- Embedded Systems

- Ethical Hacking

- Wireless Networks

- Computer Manufacturing

- Database Systems

- Web Applications

- Animation

- Computer Graphics

- Scientific Modelling

- Computational Biology

- Video Game Development

- Mobile Application Development

- Data Science

- Network Administration

 Top Professional Job Profiles for Computer Engineers

Here are the top nine job profiles for computer engineers:

1. Data Scientist

   - Data Scientists analyse and interpret large amounts of data, utilizing skills in statistics, mathematics, and computer science to structure and organize databases for various organizations.

2. System Analyst

   - System Analysts optimize and troubleshoot existing systems, suggest new programs and applications, and enhance the role of technology in organizations, working in financial, medical, IT, and government agencies.

3. Software Developer

   - Software Developers design and develop system software and applications to improve organizational performance, creating apps and games by writing code in languages like Python, Java, C, SQL, etc.

4. Hardware Engineer

   - Hardware Engineers design, develop, and test physical components of computers and technological systems, contributing to fields like robotics, AI, embedded systems, and medical sectors.

5. IT Consultant

   - IT Consultants work on projects, analyse data, determine information system requirements, recommend hardware and software, and troubleshoot issues for various sectors, including IT, finance, and medical fields.

6. Networking Engineer

   - Network Engineers create and maintain information transmission systems and networks, ensuring security and maximum infrastructure for users and organizations.

7. Database Administrator

   - Database Administrators manage and analyse databases for banks, hospitals, financial firms, government organizations, and universities.

8. Web Developer

   - Web Developers design and develop websites, ensuring technical functionality, user interface, engagement, performance, and maintenance for businesses, non-profits, and e-commerce.

9. Embedded Systems Engineer

   - Embedded Systems Engineers use software programming tools and microprocessors to control devices and machines in education, healthcare, aviation, automotive, and consumer electronics industries.

 Building a Successful Career in Computer Engineering: Tips and Hacks

1. Know Your Strengths

   - Understand why you are interested in computer engineering and what you plan to achieve with it. Identifying specific technical and soft skills is crucial for your chosen career path.

2. Graduate from a Prestigious Institution

   - Choose a degree from a well-accredited institution to enhance your employability. For those seeking the best engineering college in Kottayam, consider GIT Engineering College, a top-rank engineering college in Kottayam.

By following these guidelines, you can pave the way for a successful career in computer engineering. Embrace the vast opportunities and be part of the technological revolution shaping our world.

 Conclusion

The field of computer engineering is thriving, offering a wealth of career opportunities. By obtaining a degree from a prestigious institution like GIT Engineering College, one of the best engineering colleges in Kottayam, you can ensure a bright and successful future in this dynamic field.

For more information and to explore our programs, visit our website today. Start your journey towards a rewarding career in computer engineering with GIT Engineering College Top engineering college in Kottayam.

[mostlysignssomeportents]

"Open" "AI" isn’t

Tomorrow (19 Aug), I'm appearing at the San Diego Union-Tribune Festival of Books. I'm on a 2:30PM panel called "Return From Retirement," followed by a signing:

https://www.sandiegouniontribune.com/festivalofbooks

The crybabies who freak out about The Communist Manifesto appearing on university curriculum clearly never read it – chapter one is basically a long hymn to capitalism's flexibility and inventiveness, its ability to change form and adapt itself to everything the world throws at it and come out on top:

https://www.marxists.org/archive/marx/works/1848/communist-manifesto/ch01.htm#007

Today, leftists signal this protean capacity of capital with the -washing suffix: greenwashing, genderwashing, queerwashing, wokewashing – all the ways capital cloaks itself in liberatory, progressive values, while still serving as a force for extraction, exploitation, and political corruption.

A smart capitalist is someone who, sensing the outrage at a world run by 150 old white guys in boardrooms, proposes replacing half of them with women, queers, and people of color. This is a superficial maneuver, sure, but it's an incredibly effective one.

In "Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI," a new working paper, Meredith Whittaker, David Gray Widder and Sarah B Myers document a new kind of -washing: openwashing:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4543807

Openwashing is the trick that large "AI" companies use to evade regulation and neutralizing critics, by casting themselves as forces of ethical capitalism, committed to the virtue of openness. No one should be surprised to learn that the products of the "open" wing of an industry whose products are neither "artificial," nor "intelligent," are also not "open." Every word AI huxters say is a lie; including "and," and "the."

So what work does the "open" in "open AI" do? "Open" here is supposed to invoke the "open" in "open source," a movement that emphasizes a software development methodology that promotes code transparency, reusability and extensibility, which are three important virtues.

But "open source" itself is an offshoot of a more foundational movement, the Free Software movement, whose goal is to promote freedom, and whose method is openness. The point of software freedom was technological self-determination, the right of technology users to decide not just what their technology does, but who it does it to and who it does it for:

https://locusmag.com/2022/01/cory-doctorow-science-fiction-is-a-luddite-literature/

The open source split from free software was ostensibly driven by the need to reassure investors and businesspeople so they would join the movement. The "free" in free software is (deliberately) ambiguous, a bit of wordplay that sometimes misleads people into thinking it means "Free as in Beer" when really it means "Free as in Speech" (in Romance languages, these distinctions are captured by translating "free" as "libre" rather than "gratis").

The idea behind open source was to rebrand free software in a less ambiguous – and more instrumental – package that stressed cost-savings and software quality, as well as "ecosystem benefits" from a co-operative form of development that recruited tinkerers, independents, and rivals to contribute to a robust infrastructural commons.

But "open" doesn't merely resolve the linguistic ambiguity of libre vs gratis – it does so by removing the "liberty" from "libre," the "freedom" from "free." "Open" changes the pole-star that movement participants follow as they set their course. Rather than asking "Which course of action makes us more free?" they ask, "Which course of action makes our software better?"

Thus, by dribs and drabs, the freedom leeches out of openness. Today's tech giants have mobilized "open" to create a two-tier system: the largest tech firms enjoy broad freedom themselves – they alone get to decide how their software stack is configured. But for all of us who rely on that (increasingly unavoidable) software stack, all we have is "open": the ability to peer inside that software and see how it works, and perhaps suggest improvements to it:

https://www.youtube.com/watch?v=vBknF2yUZZ8

In the Big Tech internet, it's freedom for them, openness for us. "Openness" – transparency, reusability and extensibility – is valuable, but it shouldn't be mistaken for technological self-determination. As the tech sector becomes ever-more concentrated, the limits of openness become more apparent.

But even by those standards, the openness of "open AI" is thin gruel indeed (that goes triple for the company that calls itself "OpenAI," which is a particularly egregious openwasher).

The paper's authors start by suggesting that the "open" in "open AI" is meant to imply that an "open AI" can be scratch-built by competitors (or even hobbyists), but that this isn't true. Not only is the material that "open AI" companies publish insufficient for reproducing their products, even if those gaps were plugged, the resource burden required to do so is so intense that only the largest companies could do so.

Beyond this, the "open" parts of "open AI" are insufficient for achieving the other claimed benefits of "open AI": they don't promote auditing, or safety, or competition. Indeed, they often cut against these goals.

"Open AI" is a wordgame that exploits the malleability of "open," but also the ambiguity of the term "AI": "a grab bag of approaches, not… a technical term of art, but more … marketing and a signifier of aspirations." Hitching this vague term to "open" creates all kinds of bait-and-switch opportunities.

That's how you get Meta claiming that LLaMa2 is "open source," despite being licensed in a way that is absolutely incompatible with any widely accepted definition of the term:

https://blog.opensource.org/metas-llama-2-license-is-not-open-source/

LLaMa-2 is a particularly egregious openwashing example, but there are plenty of other ways that "open" is misleadingly applied to AI: sometimes it means you can see the source code, sometimes that you can see the training data, and sometimes that you can tune a model, all to different degrees, alone and in combination.

But even the most "open" systems can't be independently replicated, due to raw computing requirements. This isn't the fault of the AI industry – the computational intensity is a fact, not a choice – but when the AI industry claims that "open" will "democratize" AI, they are hiding the ball. People who hear these "democratization" claims (especially policymakers) are thinking about entrepreneurial kids in garages, but unless these kids have access to multi-billion-dollar data centers, they can't be "disruptors" who topple tech giants with cool new ideas. At best, they can hope to pay rent to those giants for access to their compute grids, in order to create products and services at the margin that rely on existing products, rather than displacing them.

The "open" story, with its claims of democratization, is an especially important one in the context of regulation. In Europe, where a variety of AI regulations have been proposed, the AI industry has co-opted the open source movement's hard-won narrative battles about the harms of ill-considered regulation.

For open source (and free software) advocates, many tech regulations aimed at taming large, abusive companies – such as requirements to surveil and control users to extinguish toxic behavior – wreak collateral damage on the free, open, user-centric systems that we see as superior alternatives to Big Tech. This leads to the paradoxical effect of passing regulation to "punish" Big Tech that end up simply shaving an infinitesimal percentage off the giants' profits, while destroying the small co-ops, nonprofits and startups before they can grow to be a viable alternative.

The years-long fight to get regulators to understand this risk has been waged by principled actors working for subsistence nonprofit wages or for free, and now the AI industry is capitalizing on lawmakers' hard-won consideration for collateral damage by claiming to be "open AI" and thus vulnerable to overbroad regulation.

But the "open" projects that lawmakers have been coached to value are precious because they deliver a level playing field, competition, innovation and democratization – all things that "open AI" fails to deliver. The regulations the AI industry is fighting also don't necessarily implicate the speech implications that are core to protecting free software:

https://www.eff.org/deeplinks/2015/04/remembering-case-established-code-speech

Just think about LLaMa-2. You can download it for free, along with the model weights it relies on – but not detailed specs for the data that was used in its training. And the source-code is licensed under a homebrewed license cooked up by Meta's lawyers, a license that only glancingly resembles anything from the Open Source Definition:

https://opensource.org/osd/

Core to Big Tech companies' "open AI" offerings are tools, like Meta's PyTorch and Google's TensorFlow. These tools are indeed "open source," licensed under real OSS terms. But they are designed and maintained by the companies that sponsor them, and optimize for the proprietary back-ends each company offers in its own cloud. When programmers train themselves to develop in these environments, they are gaining expertise in adding value to a monopolist's ecosystem, locking themselves in with their own expertise. This a classic example of software freedom for tech giants and open source for the rest of us.

One way to understand how "open" can produce a lock-in that "free" might prevent is to think of Android: Android is an open platform in the sense that its sourcecode is freely licensed, but the existence of Android doesn't make it any easier to challenge the mobile OS duopoly with a new mobile OS; nor does it make it easier to switch from Android to iOS and vice versa.

Another example: MongoDB, a free/open database tool that was adopted by Amazon, which subsequently forked the codebase and tuning it to work on their proprietary cloud infrastructure.

The value of open tooling as a stickytrap for creating a pool of developers who end up as sharecroppers who are glued to a specific company's closed infrastructure is well-understood and openly acknowledged by "open AI" companies. Zuckerberg boasts about how PyTorch ropes developers into Meta's stack, "when there are opportunities to make integrations with products, [so] it’s much easier to make sure that developers and other folks are compatible with the things that we need in the way that our systems work."

Tooling is a relatively obscure issue, primarily debated by developers. A much broader debate has raged over training data – how it is acquired, labeled, sorted and used. Many of the biggest "open AI" companies are totally opaque when it comes to training data. Google and OpenAI won't even say how many pieces of data went into their models' training – let alone which data they used.

Other "open AI" companies use publicly available datasets like the Pile and CommonCrawl. But you can't replicate their models by shoveling these datasets into an algorithm. Each one has to be groomed – labeled, sorted, de-duplicated, and otherwise filtered. Many "open" models merge these datasets with other, proprietary sets, in varying (and secret) proportions.

Quality filtering and labeling for training data is incredibly expensive and labor-intensive, and involves some of the most exploitative and traumatizing clickwork in the world, as poorly paid workers in the Global South make pennies for reviewing data that includes graphic violence, rape, and gore.

Not only is the product of this "data pipeline" kept a secret by "open" companies, the very nature of the pipeline is likewise cloaked in mystery, in order to obscure the exploitative labor relations it embodies (the joke that "AI" stands for "absent Indians" comes out of the South Asian clickwork industry).

The most common "open" in "open AI" is a model that arrives built and trained, which is "open" in the sense that end-users can "fine-tune" it – usually while running it on the manufacturer's own proprietary cloud hardware, under that company's supervision and surveillance. These tunable models are undocumented blobs, not the rigorously peer-reviewed transparent tools celebrated by the open source movement.

If "open" was a way to transform "free software" from an ethical proposition to an efficient methodology for developing high-quality software; then "open AI" is a way to transform "open source" into a rent-extracting black box.

Some "open AI" has slipped out of the corporate silo. Meta's LLaMa was leaked by early testers, republished on 4chan, and is now in the wild. Some exciting stuff has emerged from this, but despite this work happening outside of Meta's control, it is not without benefits to Meta. As an infamous leaked Google memo explains:

Paradoxically, the one clear winner in all of this is Meta. Because the leaked model was theirs, they have effectively garnered an entire planet's worth of free labor. Since most open source innovation is happening on top of their architecture, there is nothing stopping them from directly incorporating it into their products.

https://www.searchenginejournal.com/leaked-google-memo-admits-defeat-by-open-source-ai/486290/

Thus, "open AI" is best understood as "as free product development" for large, well-capitalized AI companies, conducted by tinkerers who will not be able to escape these giants' proprietary compute silos and opaque training corpuses, and whose work product is guaranteed to be compatible with the giants' own systems.

The instrumental story about the virtues of "open" often invoke auditability: the fact that anyone can look at the source code makes it easier for bugs to be identified. But as open source projects have learned the hard way, the fact that anyone can audit your widely used, high-stakes code doesn't mean that anyone will.

The Heartbleed vulnerability in OpenSSL was a wake-up call for the open source movement – a bug that endangered every secure webserver connection in the world, which had hidden in plain sight for years. The result was an admirable and successful effort to build institutions whose job it is to actually make use of open source transparency to conduct regular, deep, systemic audits.

In other words, "open" is a necessary, but insufficient, precondition for auditing. But when the "open AI" movement touts its "safety" thanks to its "auditability," it fails to describe any steps it is taking to replicate these auditing institutions – how they'll be constituted, funded and directed. The story starts and ends with "transparency" and then makes the unjustifiable leap to "safety," without any intermediate steps about how the one will turn into the other.

It's a Magic Underpants Gnome story, in other words:

Step One: Transparency

Step Two: ??

Step Three: Safety

https://www.youtube.com/watch?v=a5ih_TQWqCA

Meanwhile, OpenAI itself has gone on record as objecting to "burdensome mechanisms like licenses or audits" as an impediment to "innovation" – all the while arguing that these "burdensome mechanisms" should be mandatory for rival offerings that are more advanced than its own. To call this a "transparent ruse" is to do violence to good, hardworking transparent ruses all the world over:

https://openai.com/blog/governance-of-superintelligence

Some "open AI" is much more open than the industry dominating offerings. There's EleutherAI, a donor-supported nonprofit whose model comes with documentation and code, licensed Apache 2.0. There are also some smaller academic offerings: Vicuna (UCSD/CMU/Berkeley); Koala (Berkeley) and Alpaca (Stanford).

These are indeed more open (though Alpaca – which ran on a laptop – had to be withdrawn because it "hallucinated" so profusely). But to the extent that the "open AI" movement invokes (or cares about) these projects, it is in order to brandish them before hostile policymakers and say, "Won't someone please think of the academics?" These are the poster children for proposals like exempting AI from antitrust enforcement, but they're not significant players in the "open AI" industry, nor are they likely to be for so long as the largest companies are running the show:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4493900

I'm kickstarting the audiobook for "The Internet Con: How To Seize the Means of Computation," a Big Tech disassembly manual to disenshittify the web and make a new, good internet to succeed the old, good internet. It's a DRM-free book, which means Audible won't carry it, so this crowdfunder is essential. Back now to get the audio, Verso hardcover and ebook:

http://seizethemeansofcomputation.org

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

[justinspoliticalcorner]

Noah Berlatsky at Public Notice:

Donald Trump hasn’t even been inaugurated yet, and his leading supporters are already tearing at each other’s throats like a pack of frothing and foul-smelling Klansmen over whether there are any good immigrants. “Take a big step back and F**K YOURSELF in the face,” Elon Musk tweeted Friday night in defense of immigrants who worked for him, in response to a Trump supporter with a more hardline view. The spectacle of billionaire Musk, techbro Vivek Ramaswamy, would-be Goebbels Steve Bannon, and gibbering Islamophobe and Trump-whisperer Laura Loomer all screaming and bellowing at each other is entertaining in a morbid way. Acrimony is inevitable in a coalition held together by bile, hatred, and racism. And if Democrats can get their act together, they may well be able to take advantage of MAGA dissension. At the same time, it’s important not to not over-interpret the intra-Trumper feud. Racism is a lie, which means it’s always incoherent — and racist coalitions often therefore end up fighting amongst themselves about who’s in the in group and who gets targeted by the regime. But historically, these arguments at the margins have often coexisted with massive human rights abuses. Ramaswamy and Bannon may disagree about the exact trajectory of MAGA. But they can still come together to hurt a lot of people — and that is exactly what they will try to do.

For MAGA, all bigotry is not created equal

This week’s round of MAGA on MAGA violence was ignited by Loomer, who was most recently in the news for her oddly close relationship to Trump in the weeks following the Butler shooting. On December 23, Loomer attacked Sriram Krishnan, who Trump selected as an advisor on artificial intelligence, criticizing his support for H-1B visas. H-1Bs allow highly skilled workers to come to the US to work and are especially prevalent in tech, where they’re used by many Indian and Chinese engineers. Loomer tweeted that support for H-1Bs was ��not America First policy."

Musk, CEO of twitter/X, Telsa, and SpaceX, has claimed to have worked in the US on an H1-B visa himself — though there is some dispute about that — and he pushed back hard against Loomer. “There is a permanent shortage of excellent engineering talent. It is the fundamental limiting factor in Silicon Valley," Musk tweeted on Christmas. Then Ramaswamy — co-leader with Musk of Trump’s much-hyped Department of Government Efficiency — poured fuel on the fire. He tweeted that tech companies need to hire foreign workers because “our American culture has venerated mediocrity over excellence.” He went on to sneer that America “celebrates the prom queen over the math olympiad champ” and suggested Americans who have trouble getting tech jobs are “wallowing in victimhood.” In short, Ramaswamy smeared all Americans, including Trump-supporting white Americans, as lazy and mediocre — tropes usually associated with anti-Black racism. Loomer, for her part, told Musk he had only supported Trump to “protect your buddy Xi JinPing [sic].” Far right pundit Ann Coulter jumped in, arguing that Musk and Ramaswamy only wanted foreign workers because they have few labor protections and are effectively “indentured servants.” (That’s a point progressive critics have made as well.) Musk responded by calling opponents of H-1Bs “contemptible fools” and “hateful, unrepentant racists.” He also appears to have demonetized the accounts of Loomer and other rightwing critics — prompting Bannon to call Musk a “toddler.” Finally, Trump returned from the golf course on Saturday and made a policy statement. Though he’s harshly criticized the H-1B program in the past, he reversed himself and said “it’s a great program.” So Musk seems to have won for now, though who knows what Trump will say next week.

Racism is stupid

Josh Marshall of Talking Points Memo pointed out that the MAGA spat is the inevitable outcome of “Trump’s deep-seated and extreme transactionalism.” Indeed, Trump has few real policy commitments beyond self-aggrandizement and revenge. Various people — Musk, Loomer, Bannon, RFK Jr., whoever — glommed onto Trump for fame or fortune or to advance their own agendas. Now they have to fight among themselves because Trump himself doesn’t really care enough to impose a vision, much less any kind of discipline.

[...]

It should be no surprise, then, that past racist regimes had similar debates about who to target and who to exempt. For instance, in 1933, when the Nazis issued legislation to exclude Jewish people from the civil service, President Hindenburg objected strongly. He declared that excluding “my old Front soldiers” who happened to be Jewish was “utter anathema to me.” He added, “if [Jewish soldiers] were worthy of being called up to fight and bleed for Germany, they ought also to be seen as worthy of remaining in their professions to serve the Fatherland.”

Hindenburg won that fight — Jewish veterans, including those with a father or son killed in action in World War I, were exempted from expulsion.

Hitler had a much more thoroughgoing investment in ideology than Trump, to put it mildly. But even he had to negotiate initially with members of his coalition. Hitler believed that all Jewish people were enemies of the state, but that notion didn’t jibe with Hindenburg’s lived experience. Racism was incoherent and unconvincing, which meant there was no real principled ground for absolute dictats. So someone with more power like Hindenburg could force Hitler to compromise.

MAGA-on-MAGA violence over H1-B Visas has spilled over into racist attacks by the same people who make racist attacks against people of color on the daily (looking at you, Vivek Ramaswamy and Elon Musk).

[mariacallous]

In April, Elon Musk told right-wing commentator Tucker Carlson that he was starting a project to compete with ChatGPT and build “a maximum truth-seeking AI that tries to understand the nature of the universe.”

Today, Musk unveiled that new artificial intelligence venture. It’s called xAI. The company’s spare landing page repeats that goal of understanding the universe and lists 11 AI researchers—seemingly all men—who have made significant contributions to the field of AI in recent years and worked at companies including Google, DeepMind, and OpenAI.

The crew is an “all-star founding team,” according to Linxi “Jim” Fan, an AI researcher at Nvidia. “I’m really impressed by the talent density—read too many papers by them to count,” he writes in a LinkedIn post.

https://twitter.com/elonmusk/status/1679164661869182976

One of the company’s cofounders, Greg Wang, said in a tweet that xAI aims to take AI to the next level by developing a mathematical “‘theory of everything’ for large neural networks,” the machine learning technology that has dominated AI for the past decade. “This AI will enable everyone to understand our mathematical universe in ways unimaginable before,” he wrote.

Like many other new AI projects, Musk’s is motivated by concern and perhaps some FOMO over the rapid rise of ChatGPT. He has talked of xAI as a response to the bot, which he has suggested has political biases, and criticized its creator, startup OpenAI, for being secretive and too cozy with its backer Microsoft. Musk’s ill feeling is perhaps compounded by the fact that he cofounded OpenAI in 2015, but three years later severed ties with what was then a nonprofit, after reportedly failing to take full control. (The company became a for-profit venture in 2019.) And Musk has recently joined those warning that AI could pose an existential threat to humanity and entrench the power of giants like Microsoft and Google.  Musk is no stranger to making bold bets, but what little has been revealed of xAI’s goals sounds a little odd. ChatGPT and its rivals such as Google’s Bard are built on deep learning, and OpenAI’s CEO Sam Altman has said wholly new ideas are needed to push beyond existing systems. Researching the fundamentals of the technology could help find them. But much of the recent progress in AI has come from making existing systems bigger and throwing more compute power and data at them. And the sweeping changes AI is expected to deliver in tech and other industries over the next few years will come from deploying that mostly-mature technology. At this stage, xAI seems likely to lack the cloud computing power needed to match OpenAI, Microsoft, and Google. And its relatively small team of AI researchers does not look world-beating compared to the hundreds that each of those established firms can deploy on AI projects. The only person involved who has a history of working on AI risks is xAI’s sole named advisor, Dan Hendrycks, who is director of the nonprofit Center for AI Safety and coordinated a recent public statement from tech leaders about the existential threat AI may pose.

Although his supposedly giant-killing AI project is starting small, Musk does, of course, have some significant resources to draw on. The new company will work closely with Twitter and Tesla, according to the xAI website. Twitter’s data from conversations on the platform is well suited to training large language models like that behind ChatGPT, and Tesla now designs its own specialized AI chips and has significant experience building large computing clusters for AI, which could be used to boost xAI’s cloud computing power. Tesla is also building a humanoid robot, a project that could be helped by, and be helpful to, xAI in future.

But perhaps at this early stage, xAI’s reality-bending rhetoric is primarily about attracting talent. AI expertise has never been in greater demand. The most pressing problem for a new entrant, even one backed by Musk’s reputation and deep pockets, is to show it can attract the researchers needed to eventually become competitive.

The huge goals Musk has set for himself—challenging existing AI giants and protecting humanity from harmful AI—make his tiny new AI company look even smaller. Many AI researchers who are also concerned about the trajectory of AI seem to view the problem as one that requires greater transparency and collaboration, rather than a lone genius with a small band of all-stars.

[pujeetha]

Top AP Universities Offering Fintech Courses

As the financial sector rapidly embraces digital transformation, Fintech (Financial Technology) has emerged as a highly sought-after career path for students interested in technology, finance, and innovation. Several leading institutions in Andhra Pradesh now offer specialized Fintech courses to meet the growing demand. Among them, Godavari Global University (GGU) stands out as one of the top engineering universities in Andhra Pradesh offering industry-relevant Fintech education. Known for its academic excellence and tech-driven learning environment, GGU is the best engineering college in Rajahmundry for students looking to blend finance with cutting-edge technology.

As one of the best B.Tech colleges in Rajahmundry, GGU integrates Fintech into its curriculum through specialized electives and programs in Computer Science, Artificial Intelligence, and Data Science. Students are trained in blockchain technology, digital payments, cybersecurity, robo-advisory systems, and financial data analytics—skills that are critical in today’s evolving financial ecosystem. Ranked among the best B Tech colleges in Andhra Pradesh, GGU provides students with access to live projects, fintech labs, and hands-on experience with industry tools. Expert-led workshops, certification training, and collaborations with banking and fintech companies ensure students gain real-world insights.

Compared to other top engineering colleges in Andhra Pradesh, GGU’s Fintech programs stand out for their innovation, relevance, and career-focused approach. The university’s strong placement support and modern infrastructure further enhance the learning experience. For students aspiring to lead in the digital finance revolution, Godavari Global University (GGU) is the best choice among Fintech-focused universities in Andhra Pradesh.

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AI Agents and the Future of Work: Will They Replace or Empower Us?

In 2025, the workplace is transforming at a pace we’ve never seen before—thanks largely to AI agents. From smart assistants that schedule meetings to autonomous financial advisors and even content creators, AI-powered tools are rapidly becoming embedded into our daily professional lives. But with this surge in innovation comes a critical question: Are AI agents here to replace us—or empower us?

Let’s explore the evolving relationship between AI and the workforce, and what it means for your career trajectory, especially in domains like finance and investment banking.

What Are AI Agents? A Quick Recap

AI agents are software programs powered by artificial intelligence that can autonomously perform tasks, make decisions, and even interact with humans in natural language. Unlike traditional automation, these agents use advanced models like GPT-4o, Claude, and Gemini that understand context, learn from data, and evolve over time.

Examples of AI agents in 2025:

Customer service bots that resolve issues without human intervention.

Financial planning assistants that provide real-time investment insights.

AI recruiters that scan resumes and conduct initial interviews.

Legal agents drafting contracts or performing document analysis.

Industries Being Reshaped by AI Agents

AI agents are no longer confined to tech companies—they’re reshaping nearly every industry:

1. Finance and Investment Banking

AI agents can now analyze massive datasets, predict market movements, and automate reporting, fundamentally changing how financial analysts and bankers work. This has led to a demand for upskilled professionals who can work alongside AI to make better decisions.

If you're in Hyderabad and want to future-proof your career in this evolving landscape, enrolling in an investment banking course in Hyderabad can give you a competitive edge. It will equip you with core financial knowledge while introducing you to the AI tools now used across global banking.

2. Healthcare

AI agents assist in diagnostics, patient data management, and even robotic surgery. Rather than replacing doctors, they’re enhancing precision and efficiency.

3. Legal

AI agents scan legal documents, identify risk, and help in compliance—all in a fraction of the time a human would take. Law firms are now hiring tech-savvy lawyers who can manage these tools.

4. Marketing and Advertising

AI tools can write ad copy, generate design ideas, analyze campaign data, and even run A/B testing autonomously. However, human creativity and brand understanding remain irreplaceable.

Will AI Agents Replace Human Jobs?

The fear that AI will lead to mass unemployment is not new. However, historical trends suggest otherwise. Technology doesn’t eliminate jobs—it transforms them.

Here’s how:

Repetitive and low-skill tasks are being automated.

Human-centric, strategic, and creative roles are growing.

New job titles are emerging: Prompt engineers, AI ethicists, automation strategists, etc.

According to the World Economic Forum, AI is expected to create 69 million new jobs by 2027, even as it displaces around 83 million.

How AI Agents Are Empowering Professionals

AI agents aren’t just replacing tasks—they’re becoming powerful co-pilots. They empower individuals and teams to:

Make faster, data-driven decisions.

Save time on mundane tasks and focus on strategic goals.

Personalize services at scale.

Experiment, iterate, and innovate rapidly.

For example, a financial analyst using AI tools can now analyze more markets in less time and offer sharper insights—boosting both productivity and impact.

Adapting to the AI-Driven Future of Work

To thrive alongside AI agents, you’ll need a mix of technical skills, industry knowledge, and soft skills.

Key skills to master:

Data literacy: Understand how to interpret AI-generated insights.

AI tool proficiency: Familiarity with platforms like Tableau, ChatGPT, Power BI, and FinTech platforms.

Critical thinking: AI is powerful, but human judgment is still essential.

Emotional intelligence: Collaboration, empathy, and leadership can’t be automated.

If you're entering the finance world, you should look for a program that combines traditional banking skills with modern analytical and AI tools. An investment banking course in Hyderabad, especially one that emphasizes financial analytics and tech integration, can prepare you for this hybrid future.

Why Hyderabad Is Becoming a Hub for Future-Ready Finance Talent

Hyderabad is evolving into a major FinTech and AI innovation center. With top investment banks and startups establishing offices in the city, there's a growing demand for professionals who understand both finance and emerging technologies.

Enrolling in a specialized investment banking course in Hyderabad will expose you to:

Real-world financial modeling

Case studies on AI in finance

Corporate tie-ups and job placement support

Expert mentorship and project work

It’s no longer just about crunching numbers; it’s about being a strategic AI-augmented decision-maker.

Conclusion: Replace or Empower? The Choice is Ours

AI agents are here to stay. The question is not whether they will replace humans—but how we choose to work with them. In most cases, AI will take over the dull parts of our jobs and free us up to focus on what truly matters: creativity, innovation, and strategy.

So whether you're a student, professional, or career-changer, now is the time to reskill and upskill. Especially in high-impact fields like finance, investing in an investment banking course in Hyderabad can make you future-ready—and AI-resilient.

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Best B Tech in Data Science Programs

Build your future in technology with the best B Tech in Data Science courses available in India.

Get Started with the B Tech Artificial Intelligence from Alliance University

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Dr. Prakarsh Singh | UG Program Chair, Plaksha

Dr. Prakarsh Singh is the Chair Professor of Economics at Plaksha University. He was a science leader and senior economist at Amazon with work spanning subscription modelling, global talent management and streaming businesses to develop strategic insights in competition, pricing and marketing.

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Woxsen University is a premier institution offering innovative programs in various fields, including engineering and technology. Among its standout offerings is the BTech CSE Artificial Intelligence program, designed to equip students with cutting-edge knowledge and skills in AI and machine learning. The curriculum emphasizes hands-on learning, industry exposure, and research opportunities, preparing graduates for thriving careers in technology. With state-of-the-art facilities and experienced faculty, Woxsen University fosters a dynamic learning environment, making it an ideal choice for aspiring engineers in the field of artificial intelligence.

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The B.Tech Course in Artificial Intelligence offered by Mahindra University provides students with a comprehensive understanding of AI technologies and their applications. Students will learn about machine learning, deep learning, natural language processing, robotics, and more. The program combines theoretical knowledge with hands-on experience, preparing students for exciting AI development, research, and implementation careers. Visit our official website to learn more.

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AI engineering college in Kerala | The Advancement of AI and Data Science Education across India

Explore the evolution of artificial intelligence and data science education in India through GIT Engineering College AI engineering college in Kerala, renowned for its premier AI courses in Kerala. Discover our comprehensive programs preparing students for the future of technology.

Artificial Intelligence (AI) and Data Science have emerged as transformative technologies reshaping industries worldwide. In Kottayam, the demand for skilled professionals in these fields is growing rapidly, highlighting the importance of quality education and training. At GIT, a pioneer among AI and Data Science colleges in Kottayam, we delve into the evolution of these technologies and their impact on education.

 The Rise of AI and Data Science

AI and Data Science are revolutionizing sectors such as healthcare, finance, retail, and more, by leveraging data to derive insights and make informed decisions. In India, the adoption of AI has accelerated, fuelled by advancements in machine learning, natural language processing, and robotics. This technological wave underscores the need for educational institutions to equip students with advanced knowledge and skills.

 AI and Data Science Courses at GIT Engineering College

As one of the leading AI courses in Kerala, we offers comprehensive programs designed to meet industry demands. Our curriculum combines theoretical foundations with practical applications, preparing students to tackle real-world challenges. From machine learning algorithms to big data analytics, students gain hands-on experience through advanced labs and industry collaborations.

 Industry-Relevant Skills and Training

We emphasizes hands-on learning and industry exposure to ensure graduates are job-ready. Students engage in projects that address contemporary issues in AI and Data Science, enhancing their problem-solving abilities and critical thinking skills. Our faculty comprises experts who guide and mentor students, fostering a conducive learning environment.

 Career Opportunities in AI and Data Science

The demand for AI and Data Science professionals continues to soar, with lucrative career opportunities in India and abroad. Graduates from GIT Top engineering college in Kerala are well-equipped to pursue roles such as data scientist, AI engineer, machine learning specialist, and more. Our strong alumni network and placement assistance further support students in securing rewarding careers.

 GIT Engineering College: Leading the Way in AI and Data Science Education

We stands out among AI and Data Science colleges in Kottayam for several reasons:

- Advanced Infrastructure: Our campus features advanced AI and Data Science labs equipped with the latest tools and technologies.

- Expert Faculty: Experienced faculty members bring industry expertise and academic rigor to the classroom.

- Holistic Development: Beyond technical skills, we focus on soft skills, entrepreneurship, and leadership development.

- Industry Partnerships: Collaborations with leading companies provide internship opportunities and industry insights.

 Conclusion

As AI and Data Science redefine the future of technology, GIT Engineering colleges in Kottayam remains committed to fostering innovation and excellence in education. Our programs empower students to become future-ready professionals capable of driving positive change in the AI landscape.

For more information about our AI and Data Science courses and admissions, visit our website or contact us today.

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Where Ideas Take Shape: Discover the Best B Tech Colleges in Roorkee

A modern education is more than a gateway to a job—it’s the ignition for innovation, the launchpad for leadership, and the soul of societal transformation. For students who dream of designing the future through code, circuits, algorithms, and business acumen, the destination is clear: B Tech Colleges in Roorkee, especially the prestigious RIT Roorkee. This institution is synonymous with excellence, bringing together world-class faculty, a technologically advanced campus, and a curriculum that speaks the language of tomorrow’s industries.

RIT Roorkee’s B.Tech program isn’t just about engineering principles. It’s a full-fledged experience that fuses core technical education with the emotional intelligence and collaborative mindset required in today’s globalized world. Every semester is crafted to push students intellectually, technically, and socially. With options ranging from Computer Science to Mechanical and Civil Engineering, the B.Tech courses are engineered to develop not just problem-solvers, but innovators who build scalable, ethical, and intelligent systems.

However, the impact of RIT Roorkee doesn’t stop at technology. As industries evolve and business strategies become more data-driven and dynamic, management education becomes crucial. The institute rises to this demand by offering one of the most comprehensive MBA programs in the region. Structured with the perfect blend of theory, case studies, and practical applications, RIT Roorkee stands out as a leading MBA College in Uttarakhand. The program is tailored to develop leaders who can manage change, drive innovation, and transform organizations from within. With electives in marketing, HR, finance, operations, and entrepreneurship, students graduate not only with a degree but with a strategic mindset ready to tackle real-world business challenges.

In keeping with the digital transformation gripping the world, RIT Roorkee has also built a strong foundation in advanced computer applications. Students looking to enter the software and IT sectors with deep programming and systems knowledge will find RIT an ideal fit. The Master of Computer Applications program here has been crafted for those who already understand the basics and want to take their skills to a professional, cutting-edge level. With a forward-thinking curriculum and top-notch computing infrastructure, the college is ranked among the finest MCA Colleges in Dehradun. The course empowers students to master database systems, programming languages, artificial intelligence, and software project management—all of which are indispensable in today’s digitally dependent world.

Another academic offering that distinguishes RIT Roorkee from many institutions is its commitment to agricultural science and environmental sustainability. The world’s increasing population and changing climate have made agricultural innovation a necessity. That’s why RIT’s Bachelor of Science in Agriculture is more than just farming—it’s about agricultural technology, sustainable practices, agribusiness, and food innovation. The college has invested heavily in research farms, labs, and academic partnerships to enhance student learning in this field. For students passionate about creating solutions in food production, resource management, and agro-tech, RIT Roorkee BSc Agriculture is a gateway to meaningful and impactful careers.

The diversity of programs at RIT Roorkee reflects its broader educational philosophy. Whether a student aims to become a data scientist, an agricultural technologist, or a corporate strategist, the institution builds a nurturing and challenging environment where excellence is the baseline. The emphasis on ethics, leadership, and social responsibility is visible in every classroom, lab, and project.

RIT Roorkee is also home to a dedicated placement and training department that works tirelessly to create opportunities for students to connect with industry leaders. From internships to final placements, students are given career guidance, soft skills training, and regular exposure to industry trends through seminars, hackathons, and industrial visits. These real-world engagements enable students to bridge the gap between academic concepts and professional expectations, ensuring they step into their careers fully prepared.

The faculty at RIT Roorkee includes a mix of scholars, industry experts, and researchers. This rich blend means students gain knowledge that is both theoretically rigorous and practically relevant. Courses are frequently updated to reflect the latest industry standards and global developments, making RIT a living, breathing academic ecosystem rather than a static syllabus-driven institution.

RIT’s campus life complements its academic rigor. With clubs for innovation, entrepreneurship, coding, literature, culture, and sports, students are encouraged to discover who they are beyond academics. The campus itself is a modern space filled with vibrant ideas, intelligent conversations, and friendships that last a lifetime. The institute celebrates diversity and inclusion, drawing students from all over India and encouraging a culture of mutual respect and shared learning.