#neural cloud professor

[mobageheroine]

Female Professor - 女教授

【Neural Cloud】

[zeldinart]

Professor and Persicaria

Video process

[dzamie]

Detecting AI-generated research papers through "tortured phrases"

So, a recent paper found and discusses a new way to figure out if a "research paper" is, in fact, phony AI-generated nonsense. How, you may ask? The same way teachers and professors detect if you just copied your paper from online and threw a thesaurus at it!

It looks for “tortured phrases”; that is, phrases which resemble standard field-specific jargon, but seemingly mangled by a thesaurus. Here's some examples (transcript below the cut):

profound neural organization - deep neural network

(fake | counterfeit) neural organization - artificial neural network

versatile organization - mobile network

organization (ambush | assault) - network attack

organization association - network connection

(enormous | huge | immense | colossal) information - big data

information (stockroom | distribution center) - data warehouse

(counterfeit | human-made) consciousness - artificial intelligence (AI)

elite figuring - high performance computing

haze figuring - fog/mist/cloud computing

designs preparing unit - graphics processing unit (GPU)

focal preparing unit - central processing unit (CPU)

work process motor - workflow engine

facial acknowledgement - face recognition

discourse acknowledgement - voice recognition

mean square (mistake | blunder) - mean square error

mean (outright | supreme) (mistake | blunder) - mean absolute error

(motion | flag | indicator | sign | signal) to (clamor | commotion | noise) - signal to noise

worldwide parameters - global parameters

(arbitrary | irregular) get right of passage to - random access

(arbitrary | irregular) (backwoods | timberland | lush territory) - random forest

(arbitrary | irregular) esteem - random value

subterranean insect (state | province | area | region | settlement) - ant colony

underground creepy crawly (state | province | area | region | settlement) - ant colony

leftover vitality - remaining energy

territorial normal vitality - local average energy

motor vitality - kinetic energy

(credulous | innocent | gullible) Bayes - naïve Bayes

individual computerized collaborator - personal digital assistant (PDA)

[alexistudies]

january 25th, 2024

stumbled across a cute coffee shop not too far from where my PI is staying! it was adorable and the cappuccino I had was delightful.

i'm back in arizona already and with all the clouds, its giving major portland vibes HAHA.

plans today

watch stats lecture from 1/23

find some matlab tutorials so i can make some progress on this neural engineering homework

draft outline for a letter of recommendation from a professor for my NIH diversity supplement

keeping the bar low since i'm tired from my trip lol.

[frank-olivier]

AI’s Second Chance: How Geometric Deep Learning Can Help Heal Silicon Valley’s Moral Wounds

The concept of AI dates back to the early 20th century, when scientists and philosophers began to explore the possibility of creating machines that could think and learn like humans. In 1929, Makoto Nishimura, a Japanese professor and biologist, created the country's first robot, Gakutensoku, which symbolized the idea of "learning from the laws of nature." This marked the beginning of a new era in AI research. In the 1930s, John Vincent Atanasoff and Clifford Berry developed the Atanasoff-Berry Computer (ABC), a 700-pound machine that could solve 29 simultaneous linear equations. This achievement laid the foundation for future advancements in computational technology.

In the 1940s, Warren S. McCulloch and Walter H. Pitts Jr introduced the Threshold Logic Unit, a mathematical model for an artificial neuron. This innovation marked the beginning of artificial neural networks, which would go on to play a crucial role in the development of modern AI. The Threshold Logic Unit could mimic a biological neuron by receiving external inputs, processing them, and providing an output, as a function of input. This concept laid the foundation for the development of more complex neural networks, which would eventually become a cornerstone of modern AI.

Alan Turing, a British mathematician and computer scientist, made significant contributions to the development of AI. His work on the Bombe machine, which helped decipher the Enigma code during World War II, laid the foundation for machine learning theory. Turing's 1950 paper, "Computing Machinery and Intelligence," proposed the Turing Test, a challenge to determine whether a machine could think. This test, although questioned in modern times, remains a benchmark for evaluating cognitive AI systems. Turing's ideas about machines that could reason, learn, and adapt have had a lasting impact on the field of AI.

The 1950s and 1960s saw a surge in AI research, driven by the development of new technologies and the emergence of new ideas. This period, known as the "AI summer," was marked by rapid progress and innovation. The creation of the first commercial computers, the development of new programming languages, and the emergence of new research institutions all contributed to the growth of the field. The AI summer saw the development of the first AI programs, including the Logical Theorist, which was designed to simulate human reasoning, and the General Problem Solver, which was designed to solve complex problems.

The term "Artificial Intelligence" was coined by John McCarthy in 1956, during the Dartmouth Conference, a gathering of computer scientists and mathematicians. McCarthy's vision was to create machines that could simulate human intelligence, and he proposed that mathematical functions could be used to replicate human intelligence within a computer. This idea marked a significant shift in the field, as it emphasized the potential of machines to learn and adapt. McCarthy's work on the programming language LISP and his concept of "Timesharing" and distributed computing laid the groundwork for the development of the Internet and cloud computing.

By the 1970s and 1980s, the AI field began to experience a decline, known as the "AI winter." This period was marked by a lack of funding, a lack of progress, and a growing skepticism about the potential of AI. The failure of the AI program, ELIZA, which was designed to simulate human conversation, and the lack of progress in developing practical AI applications contributed to the decline of the field. The AI winter lasted for several decades, during which time AI research was largely relegated to the fringes of the computer science community.

The AI Winter was caused by a combination of factors, including overhyping and unrealistic expectations, lack of progress, and lack of funding. In the 1960s and 1970s, AI researchers had predicted that AI would revolutionize the way we live and work, but these predictions were not met. As one prominent AI researcher, John McCarthy, noted, "The AI community has been guilty of overpromising and underdelivering". The lack of progress in AI research led to a decline in funding, as policymakers and investors became increasingly skeptical about the potential of AI.

One of the primary technical challenges that led to the decline of rule-based systems was the difficulty of hand-coding rules. As the AI researcher, Marvin Minsky, noted, "The problem with rule-based systems is that they require a huge amount of hand-coding, which is time-consuming and error-prone". This led to a decline in the use of rule-based systems, as researchers turned to other approaches, such as machine learning and neural networks.

The personal computer revolutionized the way people interacted with technology, and it had a significant impact on the development of AI. The personal computer made it possible for individuals to develop their own software without the need for expensive mainframe computers, and it enabled the development of new AI applications.

The first personal computer, the Apple I, was released in 1976, and it was followed by the Apple II in 1977. The IBM PC was released in 1981, and it became the industry standard for personal computers.

The AI Winter had a significant impact on the development of AI, and it led to a decline in interest in AI research. However, it also led to a renewed focus on the fundamentals of AI, and it paved the way for the development of new approaches to AI, such as machine learning and deep learning. These approaches were developed in the 1980s and 1990s, and they have since become the foundation of modern AI.

As AI research began to revive in the late 1990s and early 2000s, Silicon Valley's tech industry experienced a moral decline. The rise of the "bro culture" and the prioritization of profits over people led to a series of scandals, including:

- The dot-com bubble and subsequent layoffs.

- The exploitation of workers, particularly in the tech industry.

- The rise of surveillance capitalism, where companies like Google and Facebook collected vast amounts of personal data without users' knowledge or consent.

This moral decline was also reflected in the increasing influence of venture capital and the prioritization of short-term gains over long-term sustainability.

Geometric deep learning is a key area of research in modern AI, and its development is a direct result of the revival of AI research in the late 1990s and early 2000s. It has the potential to address some of the moral concerns associated with the tech industry. Geometric deep learning methods can provide more transparent and interpretable results, which can help to mitigate the risks associated with AI decision-making. It can be used to develop more fair and unbiased AI systems, which can help to address issues of bias and discrimination in AI applications. And it can be used to develop more sustainable AI systems, which can help to reduce the environmental impact of AI research and deployment.

Geometric deep learning is a subfield of deep learning that focuses on the study of geometric structures and their representation in data. This field has gained significant attention in recent years, particularly in applications such as object detection, segmentation, tracking, robot perception, motion planning, control, social network analysis and recommender systems.

While Geometric Deep Learning is not a direct solution to the moral decline of Silicon Valley, it has the potential to address some of the underlying issues and promote more responsible and sustainable AI research and development.

As AI becomes increasingly integrated into our lives, it is essential that we prioritize transparency, accountability, and regulation to ensure that AI is used in a way that is consistent with societal values.

Transparency is essential for building trust in AI, and it involves making AI systems more understandable and explainable. Accountability is essential for ensuring that AI is used responsibly, and it involves holding developers and users accountable for the impact of AI. Regulation is essential for ensuring that AI is used in a way that is consistent with societal values, and it involves developing and enforcing laws and regulations that govern the development and use of AI.

Policymakers and investors have a critical role to play in shaping the future of AI. They can help to ensure that AI is developed and used in a way that is consistent with societal values by providing funding for AI research, creating regulatory frameworks, and promoting transparency and accountability.

The future of AI is uncertain, but it is clear that AI will continue to play an increasingly important role in society. As AI continues to evolve, it is essential that we prioritize transparency, accountability, and regulation to ensure that AI is used in a way that is consistent with societal values.

Prof. Gary Marcus: The AI Bubble - Will It Burst, and What Comes After? (Machine Learning Street Talk, August 2024)

Prof. Gary Marcus: Taming Silicon Valley (Machine Learning Street Talk, September 2024)

LLMs Cannot Reason (TheAIGRID, October 2024)

Geometric Deep Learning Blueprint (Machine Learning Street Talk, September 2021)

Max Tegmark’s Insights on AI and The Brain (TheAIGRID, November 2024)

Michael Bronstein: Geometric Deep Learning - The Erlangen Programme of ML (Imperial College London, January 2021)

This is why Deep Learning is really weird (Machine Learning Street Talk, December 2023)

Michael Bronstein: Geometric Deep Learning (MLSS Kraków, December 2023)

Saturday, November 2, 2024

[jcmarchi]

New AI noise-canceling headphone technology lets wearers pick which sounds they hear - Technology Org

New Post has been published on https://thedigitalinsider.com/new-ai-noise-canceling-headphone-technology-lets-wearers-pick-which-sounds-they-hear-technology-org/

New AI noise-canceling headphone technology lets wearers pick which sounds they hear - Technology Org

Most anyone who’s used noise-canceling headphones knows that hearing the right noise at the right time can be vital. Someone might want to erase car horns when working indoors but not when walking along busy streets. Yet people can’t choose what sounds their headphones cancel.

A team led by researchers at the University of Washington has developed deep-learning algorithms that let users pick which sounds filter through their headphones in real time. Pictured is co-author Malek Itani demonstrating the system. Image credit: University of Washington

Now, a team led by researchers at the University of Washington has developed deep-learning algorithms that let users pick which sounds filter through their headphones in real time. The team is calling the system “semantic hearing.” Headphones stream captured audio to a connected smartphone, which cancels all environmental sounds. Through voice commands or a smartphone app, headphone wearers can select which sounds they want to include from 20 classes, such as sirens, baby cries, speech, vacuum cleaners and bird chirps. Only the selected sounds will be played through the headphones.

The team presented its findings at UIST ’23 in San Francisco. In the future, the researchers plan to release a commercial version of the system.

[embedded content]

“Understanding what a bird sounds like and extracting it from all other sounds in an environment requires real-time intelligence that today’s noise canceling headphones haven’t achieved,” said senior author Shyam Gollakota, a UW professor in the Paul G. Allen School of Computer Science & Engineering. “The challenge is that the sounds headphone wearers hear need to sync with their visual senses. You can’t be hearing someone’s voice two seconds after they talk to you. This means the neural algorithms must process sounds in under a hundredth of a second.”

Because of this time crunch, the semantic hearing system must process sounds on a device such as a connected smartphone, instead of on more robust cloud servers. Additionally, because sounds from different directions arrive in people’s ears at different times, the system must preserve these delays and other spatial cues so people can still meaningfully perceive sounds in their environment.

Tested in environments such as offices, streets and parks, the system was able to extract sirens, bird chirps, alarms and other target sounds, while removing all other real-world noise. When 22 participants rated the system’s audio output for the target sound, they said that on average the quality improved compared to the original recording.

In some cases, the system struggled to distinguish between sounds that share many properties, such as vocal music and human speech. The researchers note that training the models on more real-world data might improve these outcomes.

Source: University of Washington

You can offer your link to a page which is relevant to the topic of this post.

[beesandwasps]

And the thing is that AI has always both (a) been crap in practice and (b) been confidently expected by computer science academics to be something they were going to perfect any day now. Like, back in the 1970s, before GUIs were a thing, academic CS types were already recommending that students devote their time to AI instead of user interface design or improving algorithms, and they’ve been doing it ever since, and that has had consequences for what has been studied, the and what “qualified” computer science types know when they graduate. I remember reading an interview from the 2000s from a professor at Northwestern University (a hotbed of pro-AI academics) admitting that if they had focussed on UI design instead of AI, they would have made the world a better place for everyone. But no, they go on and on with AI research — which never involves interdisciplinary study with biologists or neurologists to learn how the human mind works, incidentally; we have exactly one naturally occurring form of sentience to learn from, and artificial intelligence workers trying to build a second one never try to model their work on current understanding of brains, because that would be hard. We have “neural nets”, which are very loosely inspired by real brains, but nothing deeper — and techbros who want to “upload their consciousness to the cloud” are certainly not bothered by all the ways the human brain interacts with the body and its surroundings. Here’s a story published in 2006 which, now that I think of it, foreshadows modern “AI” (and I put that in quotation marks on purpose — what the public and the press refer to as AI does not actually meet the traditional qualifications) almost spookily, although I admit that even the neural net expert involved was adamant that neural nets were the wrong tool for the specific job.

And these academic choices do have consequences! Just for an example: CS types are encouraged to like functional programming languages like Lisp and Scheme and consider them elegant and superior (as opposed to procedural programming languages like… well, nearly every one you’ve likely ever to have heard of if you’re not a CS major: C, C++, C#, Java, Javascript, Perl, Ruby, Python, Rust… all procedural) in part because AI researchers love functional programming languages and want to use them (although they usually cannot actually get away with it). But in the real world, functional programming languages suck. Every attempt to produce a modern — as in “more recent than about 1985” — program for widespread practical use using functional programming has ended up being, at best, full of chunks of procedural programming to make the code efficient enough to actually be used, and usually the performance of functional programming is so bad that it has to be scrapped entirely. (And this very explicitly applies to handling large data sets, which is what AI researchers are trying to do!) When a CS academic uses the word “elegance” they mean “unbelievable inefficiency and bloat”. There’s a reason why even the crappy, overblown autocomplete AI systems we have, like ChatGPT, keep being revealed to be so wasteful of energy and physical resources. (And that, remember, is the best AI researchers can do. Maybe if the focus had been on efficiency rather than “elegance” things would be different.) But the academic bias persists — it even has shown up in XKCD, which (like his preference for input sanitizing rather than parameterization) suggests that the Munroe doesn’t actually do much practical work with computers.

The Amazon grocery stores which touted an AI system that tracked what you put in your cart so you didn't have to go through checkout were actually powered by underpaid workers in India.

Just over half of Amazon Fresh stores are equipped with Just Walk Out. The technology allows customers to skip checkout altogether by scanning a QR code when they enter the store. Though it seemed completely automated, Just Walk Out relied on more than 1,000 people in India watching and labeling videos to ensure accurate checkouts. The cashiers were simply moved off-site, and they watched you as you shopped. According to The Information, 700 out of 1,000 Just Walk Out sales required human reviewers as of 2022. This widely missed Amazon’s internal goals of reaching less than 50 reviews per 1,000 sales

A great many AI products are just schemes to shift labor costs to more exploitable workers. There may indeed be a neural net involved in the data processing pipeline, but most products need a vast and underpaid labor force to handle its nearly innumerable errors.

[williambutcher008]

Top Colleges for AI and Data Science Course in Tamil Nadu: Your 2025 Guide to Future-Ready Education

Choosing the right AI and Data Science course college in Tamil Nadu can be the most important decision you make for your future in tech. With AI transforming industries and data becoming the new oil, Tamil Nadu has emerged as a hub for institutions offering cutting-edge programs in this field. Students across the state are eager to enroll in colleges that provide not just degrees, but a launchpad to innovation, research, and top-tier placements.

In this blog, we’ll take a deep dive into what makes an AI and Data Science course college in Tamil Nadu stand out, explore the growing relevance of the course, and spotlight the institutions leading the way in 2025 — especially Mailam Engineering College, a rising star redefining how AI is taught and applied in real-world scenarios.

Why AI and Data Science Is the Future

From predicting health crises to powering self-driving cars, AI and Data Science are reshaping the world. According to industry projections, over 70% of jobs in the next decade will require AI-related skills. This has made courses in Artificial Intelligence and Data Science some of the most sought-after programs in India, especially in a tech-forward state like Tamil Nadu.

Students are no longer satisfied with traditional engineering streams. They’re looking for an education that prepares them for real-world challenges, cutting-edge technologies, and global opportunities. That’s why finding the best AI and Data Science course college in Tamil Nadu has become such a priority.

What to Look for in a Top AI and Data Science College

When you're evaluating options, don’t just look at rankings. Consider the elements that truly impact your learning experience and career prospects:

Curriculum Relevance: Does the course cover Machine Learning, Deep Learning, Data Visualization, Big Data, and Cloud Computing?

Faculty Excellence: Are the professors experienced in both academia and the industry?

Lab Facilities: Does the college provide advanced computing labs, access to real datasets, and hands-on project work?

Placement Support: Are students getting placed in companies working on real AI products, not just generic IT roles?

Industry Tie-Ups: Does the college collaborate with companies like IBM, Google, or Amazon for internships, certifications, and live projects?

These are the key aspects that separate an average college from a top-tier AI and Data Science course college in Tamil Nadu.

Mailam Engineering College: A Game Changer in AI Education

One institution that has caught the attention of students, parents, and recruiters alike is Mailam Engineering College. Located in Tamil Nadu, this college is redefining what it means to study AI and Data Science. From its industry-relevant syllabus to world-class labs, the college offers everything a tech-savvy student needs to thrive in 2025 and beyond.

Mailam Engineering College’s program in Artificial Intelligence and Data Science stands out for a few solid reasons:

Future-Proof Curriculum: Courses are carefully designed to match industry demands. Students dive into core concepts like Natural Language Processing, Computer Vision, Neural Networks, and Data Analytics using tools like Python, TensorFlow, and R.

Hands-On Learning: Every student works on live projects, whether it's predicting crop yields using satellite data or building an AI chatbot for college use. This isn’t just theory — it's real application.

Research Opportunities: The college encourages innovation and even funds student-led research. From AI in agriculture to smart healthcare systems, the projects are impactful and future-focused.

Placement Success: The college has a strong record of placing students in companies where AI is at the core of their business — not just as a side skill. Students have landed roles in data science, machine learning engineering, and AI research.

For anyone serious about joining a top AI and Data Science course college in Tamil Nadu, Mailam Engineering College is an option that deserves serious consideration.

Other Top Colleges Offering AI and Data Science in Tamil Nadu

While Mailam Engineering College is gaining momentum, several other institutions in Tamil Nadu have also made a name for themselves in this field. Here’s a brief look at some of them:

1. Anna University, Chennai

A government-run institution with a long-standing reputation. Their AI and Data Science program emphasizes research and foundational knowledge.

2. PSG College of Technology, Coimbatore

Known for academic excellence and discipline, PSG offers a comprehensive AI curriculum with industry exposure.

3. SRM Institute of Science and Technology, Kattankulathur

With global partnerships and flexible course structures, SRM provides international exposure and a strong tech culture.

4. VIT Vellore

VIT is another private university offering robust programs in AI and Data Science with a strong focus on research and development.

Each of these colleges brings something unique to the table. However, Mailam Engineering College continues to stand out for its balance of affordability, quality education, practical learning, and placements — all the markers of a great AI and Data Science course college in Tamil Nadu.

Student Voices and Real Stories

Here’s what some students from Mailam Engineering College have to say:

"I was always fascinated by how Netflix recommends movies. At Mailam, I learned the exact algorithms behind it and even built a similar recommendation engine in my third year!" — Karthik R., Final Year Student

"Thanks to the strong faculty support and hands-on projects, I got placed as a Machine Learning Engineer right after graduation. Mailam made me industry-ready." — Divya M., Alumni

These stories are not one-offs. They represent a culture of excellence that every AI and Data Science course college in Tamil Nadu should aspire to.

Final Thoughts: Your Future Starts Now

AI and Data Science aren’t just passing trends — they’re the foundation of future technologies. If you’re passionate about coding, problem-solving, and shaping the digital world, this is your path. But success in this field starts with choosing the right college.

Tamil Nadu is blessed with several quality institutions, but only a few truly prepare you for a dynamic tech career. In 2025, Mailam Engineering College is one such place that’s earning attention for all the right reasons. With its modern curriculum, expert faculty, and practical approach, it’s fast becoming the go-to AI and Data Science course college in Tamil Nadu.

Your future in AI starts with the right step — make it count.

[dypcoeakurdi]

Paving the Way to Success: Why D Y Patil College of Engineering, Akurdi is Pune’s Top Pick for AI and Data Science

Hello, future tech pioneers! As a professor deeply immersed in the fields of Artificial Intelligence (AI) and Data Science, I’ve spent years mentoring students who are passionate about transforming the world through technology. If you’re standing at the crossroads of your academic journey and wondering which college in Pune can best prepare you for a career in AI and Data Science, let me introduce you to D Y Patil College of Engineering (DYPCOE), Akurdi —a trailblazer in nurturing tomorrow’s innovators.

The Transformative Power of AI and Data Science

The 21st century belongs to those who can harness the power of data and intelligence. According to McKinsey Global Institute , AI could add up to $13 trillion to the global economy by 2030. Similarly, industries across the board—from healthcare and finance to retail and transportation—are increasingly relying on data-driven insights to drive efficiency and innovation.

Pune, with its vibrant IT ecosystem and proximity to major tech hubs, offers an ideal environment for students to immerse themselves in this exciting field. But to truly excel, you need a college that not only imparts knowledge but also equips you with the skills to tackle real-world challenges. That’s where DYPCOE shines.

Why Choose D Y Patil College of Engineering, Akurdi?

1. A Future-Ready Curriculum

One of the standout features of DYPCOE’s Artificial Intelligence and Data Science program is its forward-thinking curriculum. Launched in 2020, the program is designed to keep pace with the rapidly evolving tech landscape. It covers foundational topics like machine learning, neural networks, natural language processing, and big data analytics while also incorporating emerging trends like explainable AI, edge computing, and ethical AI practices.

What sets DYPCOE apart is its emphasis on experiential learning. Students don’t just study algorithms—they build them. For example, our students recently developed an AI-powered recommendation system for a local e-commerce startup, helping it personalize customer experiences. Such hands-on projects ensure that graduates are not just job-ready but industry-ready.

2. AICTE Approval and Savitribai Phule Pune University Affiliation

When evaluating colleges, credibility matters. DYPCOE is approved by the All India Council for Technical Education (AICTE), ensuring adherence to national standards of excellence. Its affiliation with Savitribai Phule Pune University—one of India’s most respected institutions—further enhances its reputation and provides students access to a vast network of academic resources and opportunities.

3. World-Class Infrastructure

To master AI and Data Science, you need cutting-edge tools and technologies. DYPCOE boasts state-of-the-art labs equipped with high-performance GPUs, cloud platforms, and software frameworks like TensorFlow, PyTorch, and Hadoop. These resources enable students to experiment, innovate, and push the boundaries of what’s possible.

Additionally, the campus features smart classrooms, a well-stocked library, and dedicated research facilities where students can explore niche areas like reinforcement learning, generative adversarial networks (GANs), and computer vision. Whether you’re training models or analyzing datasets, DYPCOE ensures you have everything you need to succeed.

4. Faculty Who Inspire Excellence

Behind every successful student is a team of inspiring mentors. At DYPCOE, our faculty comprises experienced academicians and industry veterans who bring a wealth of knowledge into the classroom. Many professors actively engage in research projects funded by organizations like the Department of Science and Technology (DST), exposing students to groundbreaking developments in AI.

For instance, one of our faculty-led teams recently developed a predictive analytics model for early detection of crop diseases. Students involved in this project gained invaluable experience in applying AI to solve real-world agricultural challenges—a testament to the practical relevance of our teaching approach.

5. Strong Industry Connections

DYPCOE has forged strong ties with leading companies in the tech industry, ensuring students have access to internships, live projects, and placement opportunities. During the 2022-23 placement season, over 90% of eligible students secured jobs in roles related to AI development, data engineering, and business intelligence. Companies like TCS, Infosys, Accenture, Capgemini, and Wipro regularly recruit from the campus.

The college also organizes guest lectures, workshops, and seminars featuring industry experts. Last year, we hosted a session on “AI Ethics and Bias” led by a senior data scientist from Google, sparking thought-provoking discussions among students.

6. Affordability Without Compromise

With an intake capacity of 180 students per year, DYPCOE ensures ample opportunities for deserving candidates. Moreover, the fees are competitively priced compared to private institutions offering similar programs, making quality education accessible to students from diverse backgrounds.

Beyond Academics: A Campus That Inspires Growth

While academics form the core of your college experience, personal growth and networking are equally important. At DYPCOE, students enjoy a vibrant campus life filled with tech fests, coding competitions, hackathons, and cultural events. Last year, our annual tech fest featured a keynote address by a senior AI researcher from IBM, inspiring students to think bigger and aim higher.

The serene surroundings of Akurdi provide a perfect balance of tranquility and energy, fostering an environment where creativity thrives and innovation becomes second nature.

Is DYPCOE Your Pathway to Success?

Choosing a college is more than just selecting a place to study—it’s about finding a community that nurtures your dreams and helps you achieve them. If you’re someone who aspires to solve complex problems, build intelligent systems, and make a meaningful impact on society, then D Y Patil College of Engineering, Akurdi , could be your gateway to success.

From its rigorous curriculum and world-class infrastructure to its strong industry connections and supportive faculty, every aspect of DYPCOE is designed to help you unlock your full potential. To learn more, visit the official course page .

Feel free to reach out if you’d like personalized guidance—I’m always here to help passionate learners navigate their path to success!

[zeldinart]

Just another day in Oasis

[gfl-neural-cloud]

Oh Oh Oh! Fire is a symbol of vitality and passion! Relax. There's no jack-in-the-box in the cake this time!

Happy birthday, Chelsea!

[tcet0]

Best Data Science Colleges in Mumbai – TCET - Thakur College of Engineering and Technology

With the rapid advancement of technology, data science has become one of the most sought-after fields in the industry. Mumbai, being a major educational hub, offers numerous opportunities for students aspiring to build a career in this domain. Among the leading data science colleges in Mumbai – TCET - Thakur College of Engineering and Technology stands out as a premier institution, providing top-tier education, industry exposure, and excellent career prospects.

Why Choose Data Science as a Career?

Data Science is transforming industries by enabling businesses to analyze and interpret large sets of data for strategic decision-making. Choosing the right college is crucial for gaining the right skills and knowledge. Data science colleges in Mumbai – TCET - Thakur College of Engineering and Technology provide a strong foundation in programming, statistics, and machine learning, making students job-ready for the competitive industry.

TCET - A Leader Among Data Science Colleges in Mumbai

Among various data science colleges in Mumbai, TCET - Thakur College of Engineering and Technology has emerged as a top choice for students due to its:

Advanced Curriculum: Covers key areas such as Machine Learning, Artificial Intelligence, and Big Data Analytics.

Industry Partnerships: Collaborations with top companies for internships and placements.

State-of-the-Art Infrastructure: Modern data science labs and research facilities.

Expert Faculty: Professors with strong academic backgrounds and industry experience.

Comprehensive Data Science Curriculum at TCET

The B.Tech program at data science colleges in Mumbai – TCET - Thakur College of Engineering and Technology is designed to equip students with both theoretical and practical knowledge. The curriculum includes:

Programming for Data Science (Python, R, SQL)

Machine Learning & Artificial Intelligence

Big Data Technologies & Cloud Computing

Data Visualization & Business Analytics

Deep Learning & Neural Networks

This well-structured program ensures that students gain expertise in data-driven technologies and stay ahead in the competitive job market.

Placement and Career Opportunities

One of the biggest advantages of studying at data science colleges in Mumbai – TCET - Thakur College of Engineering and Technology is the strong placement assistance. TCET has a remarkable track record of placing students in top multinational companies, startups, and research organizations. Some of the career roles available for graduates include:

Data Scientist

Machine Learning Engineer

Data Analyst

Big Data Engineer

AI Researcher

Top recruiters visiting TCET include companies like TCS, Infosys, Accenture, Capgemini, and other leading tech firms.

Infrastructure and Research Facilities

What makes data science colleges in Mumbai – TCET - Thakur College of Engineering and Technology stand out is its modern infrastructure that enhances learning and innovation. TCET provides:

AI & Data Science Labs with high-performance computing systems.

Dedicated Research Centers focusing on AI and data-driven projects.

Library with Extensive Resources for advanced learning and research.

Industry-Oriented Workshops & Seminars conducted by experts.

Conclusion

For students seeking the best data science colleges in Mumbai – TCET - Thakur College of Engineering and Technology offers the perfect blend of academic excellence, industry exposure, and career opportunities. With its top-notch curriculum, modern infrastructure, and strong placement support, TCET is the ideal destination for aspiring data science professionals.

To know more, visit - https://www.tcetmumbai.in/AI&DS/data-science-colleges-in-mumbai.html

This article is also posted on Medium - https://medium.com/@aishwaryaa0203/best-data-science-colleges-in-mumbai-tcet-thakur-college-of-engineering-and-technology-e7e09972f302

[niilmuniversity2024]

BCA in AI at NIILM University: Industry-Ready Curriculum & Future-Proof Career

Haryana is home to several universities offering BCA in Artificial Intelligence, each with its own strengths in AI education. Many institutions in the state have recognized the growing demand for AI professionals and have developed programs integrating AI, machine learning, deep learning, neural networks, and data science. These universities focus on a mix of theoretical knowledge and practical exposure through modern AI labs, industry partnerships, and real-world projects.

However, NIILM UNIVERSITY stands out as the best choice for pursuing BCA in AI in Haryana—offering an unparalleled learning experience. This institution provides a future-ready curriculum, blending AI fundamentals with advanced applications in automation, robotics, and big data analytics. It ensures students are trained on cutting-edge AI tools and platforms through hands-on projects, hackathons, and research-driven learning.

Key Features of the BCA in AI Program at NIILM UNIVERSITY:

Comprehensive Curriculum – Covers AI fundamentals, Python programming, cloud computing, natural language processing, and AI ethics.

State-of-the-Art AI Labs – Equipped with high-end computing infrastructure to facilitate research and development.

Industry Collaborations – Ties with leading tech firms offer students exposure to live projects, internships, and certifications in AI and machine learning.

Expert Faculty & Research Opportunities – Professors with industry and academic experience mentor students, encouraging AI innovations and startups.

Placement & Career Support – Dedicated career cell ensures job readiness through mock interviews, resume-building sessions, and recruitment drives with top AI companies.

Why NIILM UNIVERSITY is the Best in Haryana for BCA in AI?

It offers an AI-focused curriculum aligned with industry standards, preparing students for the demands of global tech companies.

Provides practical exposure through AI labs, capstone projects, and industry partnerships, unlike traditional universities focusing only on theory.

Strong placement support with tie-ups in leading companies like Google, Microsoft, IBM, and AI startups.

Encourages students to develop AI-powered solutions through incubation centers and research collaborations.

Future Career Opportunities After Completing BCA in AI from NIILM UNIVERSITY

Graduates from this program can explore diverse career paths, including:

AI & Machine Learning Engineer – Develop and optimize AI algorithms and models.

Data Scientist – Analyze large datasets using AI-driven tools for business insights.

AI Research Scientist – Work on cutting-edge AI innovations in research labs.

Software Developer (AI Specialization) – Build AI-powered applications and solutions.

Business Intelligence Analyst – Use AI-driven analytics to enhance decision-making.

Robotics Engineer – Design and develop AI-driven automation systems.

With the rising demand for AI professionals, this program ensures students are job-ready and future-proof, making it the best choice among all universities in Haryana for a career in artificial intelligence.

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A blending of Artificial Intelligence (AI) with Semiconductors

Dr. Ipseeta Nanda

Professor, IILM University, Greater Noida

UP, India The integration of Artificial Intelligence (AI) with the semiconductor industry represents a transformative convergence that is reshaping the technological landscape by advancing chip design, optimizing manufacturing, and enabling AI-powered applications. Semiconductors are the foundational building blocks of modern computing devices, and their role in supporting AI has become increasingly critical as AI applications demand unprecedented computational power, efficiency, and scalability. At the design stage, AI technologies, such as machine learning algorithms, are being employed to enhance Electronic Design Automation (EDA) tools.

These AI-powered tools enable engineers to accelerate chip design processes by optimizing key parameters like performance, power efficiency, and area (PPA). AI algorithms are also capable of predicting potential design flaws early in the development cycle, significantly reducing the costly iterations traditionally associated with chip manufacturing. Moreover, generative AI is being explored to automate the creation of chip layouts, offering innovative design solutions that may not be immediately intuitive to human designers.

This not only shortens the time-to-market for advanced chips but also fosters creativity in architecture exploration. On the manufacturing side, AI-driven solutions are proving instrumental in optimizing semiconductor fabrication processes. Fabrication involves numerous intricate steps, from deposition and etching to lithography and packaging, each requiring precise control to ensure high yield and minimal defects. AI systems monitor these processes in real time, analyzing vast streams of data to identify inefficiencies or abnormalities that human operators might overlook. For example, predictive maintenance powered by machine learning can analyze sensor data from fabrication equipment to foresee potential malfunctions, allowing proactive measures to prevent costly downtime.

Similarly, advanced AI models are used in defect detection systems to analyze optical inspection data, classifying defects with a level of speed and accuracy that surpasses traditional rule-based systems. These advancements translate into higher manufacturing efficiency, reduced waste, and improved product quality, which are critical in a highly competitive industry.

Another important aspect of this integration is the development of AI-specific semiconductor architectures that enhance the performance of AI workloads. Traditional general-purpose CPUs are no longer sufficient to meet the demands of modern AI algorithms, which require massive parallel processing and high memory bandwidth. This has led to the emergence of specialized hardware such as Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and custom-designed Application-Specific Integrated Circuits (ASICs).

These chips are optimized to handle the intensive computations required for training deep neural networks and performing real-time inference. Beyond this, innovations in neuromorphic computing are paving the way for chips that mimic the structure and function of the human brain. Such architectures are especially promising for edge AI applications, where low latency and energy efficiency are paramount. Edge devices, including smartphones, smart sensors, and autonomous vehicles, are increasingly equipped with AI-optimized semiconductors that allow complex models to run locally without relying on cloud-based resources.

This not only reduces the time required for decision-making but also addresses privacy concerns by keeping data processing on-device. Furthermore, the symbiotic relationship between AI and semiconductors is enabling advancements in sectors like healthcare, automation, and telecommunications. In healthcare, for instance, AI-powered chips are revolutionizing medical imaging by enhancing the resolution and speed of imaging devices, leading to faster and more accurate diagnoses. Robotic surgery systems also benefit from AI-enabled chips that provide the computational power needed for precise, real-time control.

Similarly, in industrial automation, semiconductors designed for AI applications drive the intelligence behind smart factories, enabling predictive maintenance, quality control, and supply chain optimization. Telecommunications is another area where this blending is having a profound impact. AI algorithms, running on advanced semiconductors, are being used to optimize network performance in 5G systems, reducing latency and improving data throughput.

Figure: Enabling AI-Powered Solutions with Semiconductors

These innovations set the stage for the next generation of connectivity technologies, paving the way for even more complex and immersive AI applications. The co-design of hardware and software is another critical element in the successful blending of AI with semiconductors. Traditionally, hardware was developed independently of software, but the increasing complexity of AI workloads has necessitated a more integrated approach. AI models and semiconductor hardware are now being co-optimized to achieve the best possible performance. This involves tailoring chip architectures to specific machine learning tasks while simultaneously adapting AI algorithms to leverage hardware capabilities fully. This synergy not only boosts computational efficiency but also addresses one of the most pressing challenges in AI today: energy consumption.

Training and deploying AI models, particularly large-scale ones, require immense amounts of energy, and semiconductors designed with energy efficiency in mind are key to making AI more sustainable. Low-power designs, combined with innovations in cooling and power management, are helping to reduce the environmental impact of AI applications. Lastly, the feedback loop between AI and semiconductor development is creating a virtuous cycle of innovation.

AI algorithms are not only used to improve semiconductor design and manufacturing but also rely on advancements in semiconductor technology to evolve further. For example, as semiconductor manufacturing enables smaller and more efficient transistors, AI models can become more complex and capable, leading to breakthroughs in fields like natural language processing, computer vision, and autonomous systems. In turn, these advancements spur demand for even more sophisticated semiconductors, driving further innovation in the industry.

This dynamic interplay is accelerating the pace of technological progress, making AI and semiconductors mutually reinforcing pillars of the modern digital era. As AI continues to evolve, the semiconductor industry is poised to play an even more pivotal role in enabling its adoption across a broad spectrum of applications, from consumer electronics to critical infrastructure. The blending of AI with semiconductors, therefore, is not just a technological trend but a foundational shift that is shaping the future of innovation across multiple domains.

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New security protocol shields data from attackers during cloud-based computation

New Post has been published on https://sunalei.org/news/new-security-protocol-shields-data-from-attackers-during-cloud-based-computation/

New security protocol shields data from attackers during cloud-based computation

Deep-learning models are being used in many fields, from health care diagnostics to financial forecasting. However, these models are so computationally intensive that they require the use of powerful cloud-based servers.

This reliance on cloud computing poses significant security risks, particularly in areas like health care, where hospitals may be hesitant to use AI tools to analyze confidential patient data due to privacy concerns.

To tackle this pressing issue, MIT researchers have developed a security protocol that leverages the quantum properties of light to guarantee that data sent to and from a cloud server remain secure during deep-learning computations.

By encoding data into the laser light used in fiber optic communications systems, the protocol exploits the fundamental principles of quantum mechanics, making it impossible for attackers to copy or intercept the information without detection.

Moreover, the technique guarantees security without compromising the accuracy of the deep-learning models. In tests, the researcher demonstrated that their protocol could maintain 96 percent accuracy while ensuring robust security measures.

“Deep learning models like GPT-4 have unprecedented capabilities but require massive computational resources. Our protocol enables users to harness these powerful models without compromising the privacy of their data or the proprietary nature of the models themselves,” says Kfir Sulimany, an MIT postdoc in the Research Laboratory for Electronics (RLE) and lead author of a paper on this security protocol.

Sulimany is joined on the paper by Sri Krishna Vadlamani, an MIT postdoc; Ryan Hamerly, a former postdoc now at NTT Research, Inc.; Prahlad Iyengar, an electrical engineering and computer science (EECS) graduate student; and senior author Dirk Englund, a professor in EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE. The research was recently presented at Annual Conference on Quantum Cryptography.

A two-way street for security in deep learning

The cloud-based computation scenario the researchers focused on involves two parties — a client that has confidential data, like medical images, and a central server that controls a deep learning model.

The client wants to use the deep-learning model to make a prediction, such as whether a patient has cancer based on medical images, without revealing information about the patient.

In this scenario, sensitive data must be sent to generate a prediction. However, during the process the patient data must remain secure.

Also, the server does not want to reveal any parts of the proprietary model that a company like OpenAI spent years and millions of dollars building.

“Both parties have something they want to hide,” adds Vadlamani.

In digital computation, a bad actor could easily copy the data sent from the server or the client.

Quantum information, on the other hand, cannot be perfectly copied. The researchers leverage this property, known as the no-cloning principle, in their security protocol.

For the researchers’ protocol, the server encodes the weights of a deep neural network into an optical field using laser light.

A neural network is a deep-learning model that consists of layers of interconnected nodes, or neurons, that perform computation on data. The weights are the components of the model that do the mathematical operations on each input, one layer at a time. The output of one layer is fed into the next layer until the final layer generates a prediction.

The server transmits the network’s weights to the client, which implements operations to get a result based on their private data. The data remain shielded from the server.

At the same time, the security protocol allows the client to measure only one result, and it prevents the client from copying the weights because of the quantum nature of light.

Once the client feeds the first result into the next layer, the protocol is designed to cancel out the first layer so the client can’t learn anything else about the model.

“Instead of measuring all the incoming light from the server, the client only measures the light that is necessary to run the deep neural network and feed the result into the next layer. Then the client sends the residual light back to the server for security checks,” Sulimany explains.

Due to the no-cloning theorem, the client unavoidably applies tiny errors to the model while measuring its result. When the server receives the residual light from the client, the server can measure these errors to determine if any information was leaked. Importantly, this residual light is proven to not reveal the client data.

A practical protocol

Modern telecommunications equipment typically relies on optical fibers to transfer information because of the need to support massive bandwidth over long distances. Because this equipment already incorporates optical lasers, the researchers can encode data into light for their security protocol without any special hardware.

When they tested their approach, the researchers found that it could guarantee security for server and client while enabling the deep neural network to achieve 96 percent accuracy.

The tiny bit of information about the model that leaks when the client performs operations amounts to less than 10 percent of what an adversary would need to recover any hidden information. Working in the other direction, a malicious server could only obtain about 1 percent of the information it would need to steal the client’s data.

“You can be guaranteed that it is secure in both ways — from the client to the server and from the server to the client,” Sulimany says.

“A few years ago, when we developed our demonstration of distributed machine learning inference between MIT’s main campus and MIT Lincoln Laboratory, it dawned on me that we could do something entirely new to provide physical-layer security, building on years of quantum cryptography work that had also been shown on that testbed,” says Englund. “However, there were many deep theoretical challenges that had to be overcome to see if this prospect of privacy-guaranteed distributed machine learning could be realized. This didn’t become possible until Kfir joined our team, as Kfir uniquely understood the experimental as well as theory components to develop the unified framework underpinning this work.”

In the future, the researchers want to study how this protocol could be applied to a technique called federated learning, where multiple parties use their data to train a central deep-learning model. It could also be used in quantum operations, rather than the classical operations they studied for this work, which could provide advantages in both accuracy and security.

“This work combines in a clever and intriguing way techniques drawing from fields that do not usually meet, in particular, deep learning and quantum key distribution. By using methods from the latter, it adds a security layer to the former, while also allowing for what appears to be a realistic implementation. This can be interesting for preserving privacy in distributed architectures. I am looking forward to seeing how the protocol behaves under experimental imperfections and its practical realization,” says Eleni Diamanti, a CNRS research director at Sorbonne University in Paris, who was not involved with this work.

This work was supported, in part, by the Israeli Council for Higher Education and the Zuckerman STEM Leadership Program.