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VentureBeat
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Large language overkill: How SLMs can beat their bigger, resource-intensive cousins
- Specialized Language Models (SLMs) could play a larger, complementary role in enterprise IT by supplementing large language models (LLMs). SLMs are perfect for performing specialized work that needs more accuracy, consistency, and transparency than LLMs. Unlike LLMs, they are trained on domain-specific data, so they have contextual intelligence to deliver more consistent, predictable, and relevant responses.
- LLMs are not specifically designed for general-purpose tasks, which leads to their tendency to make errors in certain specialized professions such as healthcare, legal, and financial services that require high levels of accuracy. Overreliance on LLMs can lead to complacency and have significant financial consequences or life-or-death repercussions.
- SLMs are better suited to address the limitations of LLMs. They are more explainable, can perform faster, and offer businesses more control over data privacy and security, especially if they are used internally or built specifically for the enterprise. SLMs are developed with a narrower focus and trained on domain-specific data, which gives them contextual intelligence to deliver more consistent, predictable, and relevant responses.
- LLMs and SLMs are not mutually exclusive. In practice, SLMs can augment LLMs, creating hybrid solutions where LLMs provide broader context and SLMs ensure precise execution. It is also essential to have a clear understanding of what use cases to tackle and the necessary skills required to train, fine-tune, and test SLMs.
- AJ Sunder, a co-founder, CIO, and CPO at Responsive, advises technology leaders to continue exploring the possibilities of LLMs. While LLMs can scale problem well, SLMs may not transfer well to certain use cases. Therefore, vetting partners and starting small, testing often, and building on early successes are crucial.
- Investing in SLMs gives companies the opportunity to future-proof their AI strategies, ensure their tools drive innovation, meet the demands of trust, reliability, and control, and stay relevant in today's world.
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Medium
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Leet Code 101: How to Solve the “Final Price With a Special Discount” Problem
- The problem is about finding the discount on each candy by comparing it with the next candy with an equal or lower price.
- A brute force solution is explained where each index is looped through to find a candy with a lower or equal price.
- An alternative approach using a monotonic stack is recommended to find the next greater element for each candy.
- The implementation of the monotonic stack approach is provided, which achieves a faster runtime.
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Medium
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STON.fi Now Integrated with Tobi: A New Era for AI-Driven Crypto Trading
- STON.fi has been integrated with Tobi, enabling seamless token swaps on the TON Blockchain.
- The integration brings faster and more efficient token swaps, enhanced AI-powered trading solutions, and a secure, decentralized wallet experience.
- Users can interact with Tobi on Telegram to discover these innovative features.
- STON.fi SDK can be utilized by TON projects to enhance their blockchain capabilities.
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Medium
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How to crack a Microsoft interview
- After applying, he got an online coding test Codility.
- The test had 2 coding questions, and had 90 minutes to solve them.
- Interview Round 1 (Technical Interview): Topics discussed were data structures and algorithms.
- The interviewer asked questions related to connecting the 'previous' pointer of a binary tree and spiral order printing of a matrix.
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Medium
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Recently, Google fired the entire Python team. Is Python still relevant in the age of AI
- Python continues to be one of the best programming languages globally, despite the recent decision by Google to lay off its entire Python team in the US.
- Here are some reasons why Python is still relevant:
- 1. Easy to learn and use: Python has a simple syntax, making it beginner-friendly.
- 2. Free and open-source: Python is freely available for use.
- 3. Versatile: Python can be used on various platforms.
- 4. Powerful: Python is widely used in data science, web development, and machine learning applications.
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Medium
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Why AI Integration in Boardrooms is Reshaping Governance
- AI integration in boardrooms is reshaping governance.
- AI tools like Aiden Insight provide real-time insights for decision-making.
- AI technology enhances governance by leveraging comprehensive data.
- AI balances human intuition and improves the decision-making process.
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Medium
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The Dark Side of Data Privacy Laws: Are They Really Helping?
- For small businesses, data compliance often feels like navigating a maze without a map.
- Privacy Laws May Favor Big Tech. Ironically, the very laws designed to rein in Big Tech often entrench their dominance.
- User Experience Takes a Hit. Cookie pop-ups and consent fatigue result in users giving data without fully understanding the terms and conditions.
- Data Localization and the Internet Divide. Data localization requirements lead to inefficiencies and limited access to global platforms in underdeveloped areas.
- Fear Stifles Innovation. Privacy regulations create gray areas that hinder businesses from innovating.
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Medium
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Top 5 Real-World Applications of Data Science
- Data science is transforming industries and making our lives easier.
- Real-world applications of data science include healthcare, retail, finance, transportation, and entertainment.
- In healthcare, data science enhances diagnosis and treatment through predictive analytics.
- Retail uses data science to personalize the shopping experience based on consumer behavior.
- Finance relies on data science for fraud detection and risk management.
- Transportation optimizes routes and improves efficiency using data science.
- Entertainment uses data science to shape content and enhance user experience.
- Data science is a game-changer with endless possibilities for future applications.
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Analyticsindiamag
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Italy Fines OpenAI 15 Million Euros Over ChatGPT Privacy Violations
- Italy's Data Protection Authority (Garante) has fined OpenAI €15.58 million for privacy violations.
- OpenAI processed users' personal data without a legal basis and violated transparency principles.
- OpenAI failed to comply with transparency requirements and lacked an effective age verification system.
- In addition to the fine, OpenAI is ordered to run a public awareness campaign in Italian media.
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Medium
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What is Data Science
- Data science combines statistics, computer science, and specialized knowledge to extract insights from data. It helps businesses, governments, and organizations make informed decisions backed by evidence.
- Data analysis has evolved into data science, especially with the growth of big data and advancements in machine learning. Data science uses rich, dynamic datasets that can change at any moment.
- Data scientists are equipped with a toolbox of skills, including programming, statistical analysis, and data visualization. They collaborate with domain experts, software engineers, and business analysts to ensure data-driven decisions are effective.
- Data collection involves gathering structured or unstructured data from sources like surveys, sensors, and public records. For accuracy, data cleaning is crucial.
- Data analysis comes in different types, including descriptive, predictive, and prescriptive analytics. Data visualization through tools like Tableau and Matplotlib simplifies results.
- Machine learning involves teaching machines how to learn from data. Supervised learning teaches the model from known outcomes; unsupervised learning identifies patterns without prior labeling techniques like decision trees and neural networks.
- Data science has practical applications in businesses, healthcare, and social impact, enhancing efficiency, developing innovative solutions, and addressing societal challenges.
- Data quality and ethics are crucial in data science. Maintaining high-quality data through regular audits and validation checks while upholding ethical data collection and usage is important.
- Data science is ever-changing, with new tech emerging regularly. Continuous learning through online courses, workshops, and community forums is essential.
- The future of data science includes rising trends such as automated data science tools and the growing power and potential of data science across various sectors. Professionals can explore various career options through specializations like data engineering or machine learning.
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Medium
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Introduction to Python Programming Language
- Python is a powerful and versatile programming language that is widely used in various industries.
- It has a simple syntax and is easy to learn, making it a great choice for beginners.
- Python can be used for web development, data analysis, machine learning, and automation.
- It supports a large and active community, as well as numerous libraries and frameworks.
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Medium
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Traits of a True Man: What Real Love Should Look Like
- A real man asks about your day and genuinely cares about the answer.
- A real man respects your boundaries and never forces you to do anything you're not ready to do.
- He makes time for you and takes that time to learn and understand who you are as a person.
- A real man consistently shows you the definition of effort with every day that passes.
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Analyticsindiamag
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‘AI Research at Indian Universities Feels like a Solo Journey’
- Funding remains one of the most significant challenges in accelerating AI research in India. The ICICLE project is an example of research in the US advancing the state-of-the-art, but the same isn't true in India. There is a massive funding gap between Indian and Western universities, and few institutions are putting students' research first. Amit Sheth, director of the (AI Institute, University of South Carolina) AIISC, highlighted the issue of a publication racket, with only a handful of researchers standing out as exceptions. Renjith Prasad, a teaching assistant at AIISC, said that the core reason lies in culture.
- Professor and university distinguished scholar of CS and engineering at Ohio State University highlighted $20 million NSF-funded AI Institute Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE), which is focused on building the next generation of high-performance compute for big data, machine learning, and the future of deep learning. The focus is simply on the democratisation of AI solutions instead of keeping it in check by a few big players like Microsoft, Google, and others.
- The biggest factor in promoting meaningful AI research in India is the massive funding gap between Indian and Western universities. Indians often have limited resources, which makes it hard to take on long-term or foundational projects. While the ideas are there, it depends on resources, manpower, and the right team with the right expertise. The researchers need to be trained so that they can move to the right field at the right time.
- Only a handful of universities in India are able to publish research in top conferences. The gap is non-existent, or very small, in the universities where students' research is given priority.
- In India, research often feels like an afterthought. Professors are often hesitant to invest deeply in fostering long-term research, thus leading students to believe the only focus is to land the best package after graduation. The education system is set up in a fashion to churn out developers rather than adapting to new times. The whole environment is outdated, with limited collaborative support.
- AI researchers at Indian universities feel like they are on a solo journey due to a lack of motivation among researchers to pursue revolutionary innovations. Even though researchers found out ways to motivate students and researchers across the globe to solve problems, it depends on resources, such as manpower.
- Amit Sheth, director of (AI Institute, University of South Carolina) AIISC, noted that the gap is non-existent or very small in only a handful of universities. According to him, enterprising students at those universities often reach out to US groups to do online internships.
- The education system is set up to churn out developers instead of fostering long-term research, which may reduce diversity in AI systems, leading to the development of systems that often become outdated in just a few years.
- Indian Universities compare poorly with Western universities, where there is a strong ecosystem enabling the development of potent solutions. Many Indian researchers need to be trained to move to the right field at the right moment.
- Indian researchers should shift beyond Ph.D.s, beyond coaching and academia, to a change in mindset from parents and founders to international investors.
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Analyticsindiamag
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OpenAI soft-launches AGI with o3 models, Enters Next Phase of AI
- OpenAI's o3 models have achieved the ARC-AGI benchmark surpassing human-level performance and achieving a score of 87.5%.
- Francois Chollet has challenged OpenAI's claim of achieving AGI with o3 models.
- The new frontier models o3 and o3 Mini will be accessible to researchers for public safety testing.
- OpenAI co-founder Ilya Sutskever's claims that the era of pretraining has officially ended and RL architecture will be actively used to scale reasoning capabilities.
- OpenAI scored 75.7% on the ARC-AGI semi-private set and 87.5% on high-compute settings, surpassing the 85% human-level performance threshold.
- OpenAI's o3 also achieved impressive results in other benchmarks, including software engineering and mathematical tests.
- OpenAI also introduced the concept of deliberative alignment, a new safety technique that uses o3's advanced reasoning capabilities to identify and reject unsafe prompts more effectively.
- The need for trust and safety has led to incubators funding startups that solve for a post-AGI world and systemic changes such as Universal Basic Income (UBI) and Universal Basic Compute (UBC).
- The foundation for this new reality will be where GDP will grow because of AI, and not extra work hours.
- Future technological advancements like Universal Basic Robot (UBR) is also beginning to become a huge theme for 2025.
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Analyticsindiamag
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OpenAI Soft-launches AGI with o3 Models, Enters Next Phase of AI
- OpenAI soft-launched AGI through o3 and o3 Mini models, which achieved nearly 90% on the ARC-AGI benchmark, surpassing human performance and exceeding expectations of Francois Chollet, creator of the benchmark.
- The upgraded ARC-AGI benchmark 2 will be available to researchers for public safety testing in the coming year.
- While these new models pointing towards AGI, the creator of the ARC-AGI benchmark believes that there are still some easy tasks that these models can't solve.
- O3 Mini releases in January 2025.
- Reinforcement Learning (RL) architecture is OpenAI's bet for scaling reasoning capabilities.
- OpenAI's o3 model beat ARC-AGI benchmark on a record high score of 87.5%.
- OpenAI's o3 model also achieved 71.7% accuracy on SWE Bench Verified and a 25% accuracy on the toughest mathematical test available, Epic AI Frontier Math Benchmark.
- OpenAI's o3 Mini offers API features like remaining battery life, percentage probability distribution, and so on.
- OpenAI is emphasizing even more on safety testing as it is proceeding towards AGI. It introduced the concept of deliberative alignment to this end.
- Incubators like Y Combinator are funding startups that solve for a post-AGI world, including space tech, energy-efficient computing, government software and so on.
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