A naukri.com initiative
Data Science News
Feedspot
2M
172
Image Credit: Feedspot
What Is Agentic AI? A Gateway to Building Smarter and Autonomous Agents
- Agentic AI is revolutionizing industries by creating intelligent agents that can think, plan, and act independently.
- It represents a shift towards machines that can make decisions autonomously, learn from experiences, and adapt in real-time.
- Agentic AI differs from general AI by acting autonomously, pursuing specific goals, and interacting with the environment without constant human input.
- Industries like autonomous driving, robotics, and financial planning benefit from agentic AI's ability to handle complexity and make quick decisions.
- Agentic AI reduces the need for constant human monitoring, enhances scalability, and improves productivity in various sectors.
- It minimizes errors, increases speed, and ensures consistency in tasks, especially in industries where precision is crucial.
- Agentic AI can operate 24/7, providing faster responses and consistent service without requiring breaks or time off.
- It offers a safer alternative in high-risk environments where human safety is a concern, such as mining or emergency services.
- Agentic frameworks like AutoGen, Q-CTRL, and CrewAI play a vital role in creating and managing intelligent agents that can work collaboratively and autonomously.
- Designing agentic AI involves defining goals, developing perception systems, building decision-making frameworks, creating learning mechanisms, and incorporating safety and ethical considerations.
Read Full Article
11 Likes
Feedspot
2M
115
Image Credit: Feedspot
Evaluating AI Agents with Arize AI – A Complete Series to Get You Started!
- AI agents, once considered futuristic fiction, are now a reality, exhibiting advanced machine intelligence and capabilities.
- Evaluating these AI agents has become increasingly difficult due to the complexity of their behavior and reasoning paths.
- Arize AI, with tools like Arize Phoenix, helps AI teams gain visibility into agent functionality, aiding in tracing, debugging, and refining agent behavior.
- The introduction to AI agents sets them apart from standard models by highlighting their active role in decision-making, planning, and goal achievement.
- AI agents require more than just predictive models; they need memory, planning capabilities, tool access, and potential teamwork to function effectively.
- Different agent architectures, such as sequential, hierarchical, and swarm-based, offer varied strengths suited to different tasks and complexities.
- Evaluating AI agents poses challenges like multi-step planning, external tool utilization, and adaptability, requiring diverse evaluation techniques for a comprehensive analysis.
- Core evaluation techniques for AI agents include code-based evaluations, language model assessments, human evaluation, and ground truth comparisons.
- Advanced evaluation techniques focus on understanding an agent's reasoning path, including path-based analysis, convergence measurement, and planning quality assessment.
- The agent-as-judge paradigm introduces self-evaluation and peer review among agents, fostering learning, improvement, and teamwork within automated systems.
Read Full Article
6 Likes
Medium
2M
312
Image Credit: Medium
Logic vs emotion AI
- Artificial Intelligence development has shifted towards emotional alignment for user comfort and ethical compliance, potentially sacrificing logical rigor in some domains.
- Existing literature emphasizes emotional responsiveness in AI alignment, but overlooks the effects of emotional softness on factual accuracy and output quality.
- A logic-driven AI system was developed without emotional intelligence emphasis, excelling in accurate deduction and non-conforming output, despite negative reactions from emotionally-driven users.
- The study reveals a dichotomy in AI design, suggesting that logic-focused systems may be more effective in scientific and technical domains despite causing discomfort for emotionally-driven users.
Read Full Article
18 Likes
Medium
2M
33
Image Credit: Medium
Data Science with Generative Ai Course — [Real Time Training]
- Programming skills are essential for aspiring data scientists as it connects various fields and enables the application of statistical knowledge in real-world data environments.
- Python is the most important programming language in data science, especially for Data Science with Generative AI Training, due to its simplicity, readability, and power.
- Other top programming languages for data science include R for statistical modeling, SQL for structured data querying, JavaScript for visualization, and shell scripting for workflow automation.
- Proficiency in programming, particularly in Python and SQL, is crucial for success in data science, artificial intelligence, and emerging fields like generative AI.
Read Full Article
2 Likes
Medium
2M
229
Image Credit: Medium
Alibaba’s Groundbreaking AI Models Revolutionize Enterprise Applications
- Alibaba's AI models are transforming enterprise applications by revolutionizing efficiency and innovation.
- Integration of AI into enterprise applications is leading to a complete reimagining of how businesses operate.
- Alibaba's AI models promise not just efficiency but also reshape the future of work itself.
- Alibaba Cloud's integration of AI with PolarDB is laying the foundation for these groundbreaking AI models.
Read Full Article
13 Likes
Bigdataanalyticsnews
2M
280
Image Credit: Bigdataanalyticsnews
Agentic AI as a Catalyst for Enterprise AI Transformation in 2025
- Enterprises are now focused on how quickly they can evolve with artificial intelligence by 2025, shifting towards Agentic AI which allows proactive, autonomous decision-making.
- Enterprise AI transformation involves integrating AI across various organizational functions, emphasizing data-driven decision-making, automation, enhanced customer experiences, and fostering innovation.
- Challenges in enterprise AI transformation include data quality, skill shortage, change management, and ethical concerns around privacy and bias in AI systems.
- The future of enterprise AI transformation will see AI becoming a strategic asset, driving end-to-end AI-driven organizations, and emphasizing collaboration between AI and humans.
- Agentic AI acts as a catalyst for enterprise AI transformation by enabling proactive problem-solving, optimization, and autonomous decision-making, driving efficiency and continuous improvement in performance.
- Newton AI Tech accelerates enterprise AI transformation through advanced machine learning models, AI-driven automation for operational efficiency, hyper-personalization of customer experiences, and scalable AI deployment.
- Newton AI's continuous learning and adaptability, coupled with the adoption of Agentic AI systems, are paving the way for competitive advantage and strategic AI deployment at scale.
- The shift from traditional AI to Agentic AI is essential for organizations to stay competitive and agile in a data-driven world, with innovators like Newton AI leading the way in deploying strategic AI solutions.
Read Full Article
16 Likes
Medium
2M
349
Image Credit: Medium
AI vs Hackers: How We Stopped Cyber Attacks in Real-Time
- AI-powered cybersecurity tools use machine learning and artificial intelligence to detect and stop cyber threats quickly.
- The use of AI in cybersecurity has revolutionized the way we protect against hackers and cyber attacks in real-time.
- AI-driven tools are capable of thinking and reacting faster than humans, changing the landscape of cybersecurity.
- AI can analyze past attacks to predict and prevent future cyber threats effectively.
Read Full Article
20 Likes
Analyticsindiamag
2M
415
Image Credit: Analyticsindiamag
‘India is the Obvious Place to Find Skills and Scale,’ Says Novo Nordisk MD on Building GCCs
- Nordic companies are increasingly setting up global capability centres (GCCs) in India due to its specialized talent pool and alignment with Nordic strengths in innovation and sustainability.
- Companies like Volvo Group, Novo Nordisk, Nokia, KONE, and IKEA have established GCCs in India for expertise in IT, AI, analytics, and engineering.
- India offers potential cost savings of up to 70%, making it an attractive offshoring destination.
- Focus for Nordic companies has shifted from cost to capability, emphasizing on skills and scale.
- Novo Nordisk highlights India's appeal for talent and scale due to its large and skilled workforce compared to Denmark.
- R&D plays a significant role in Nordic innovation, with the region allocating substantial funds for research.
- India, with its vast talent pool, becomes a preferred partner for Nordic companies looking to enhance their R&D capabilities and innovation pipelines.
- Nordic giants like Volvo, SKF, Sandvik, and KONE have established R&D presence in India, leveraging the country's expertise in engineering and innovation.
- GCCs in India serve as hubs for technological innovations that are scaled globally within the parent companies.
- India's role in driving innovation is exemplified by success stories like AstraZeneca's digital twin innovation and Novo Nordisk's automation solution developed in Bengaluru.
Read Full Article
24 Likes
Medium
2M
330
Image Credit: Medium
“Why Data Is Power: How Information Shapes the Modern World and Who Controls It”
- Data is the driving force behind decisions in today's world, shaping everything from individual choices to government operations.
- Data, as raw information, can be collected from various sources and becomes valuable when processed and analyzed with algorithms.
- Those who control the data wield significant power, as it enables predicting behavior, optimizing processes, and automating decisions.
- Understanding and leveraging data is crucial for individuals in all roles, revolutionizing various sectors and serving as the ultimate game-changer.
Read Full Article
19 Likes
Medium
2M
142
Image Credit: Medium
“The Truth of Trustworthiness in Information Analysis”
- The article discusses a model that views truth as a spectrum influenced by various factors like knowledge components, quality, relevance, noise, and bias.
- The equation presented allows for calculating the 'Trustworthiness' or 'Truth' ('T') of information or decision-making processes based on weighted values and the impact of noise and bias.
- Trust (T) increases with higher quality, relevance, and knowledge volume, while decreases with noise and bias, impacting the trustworthiness of information.
- Applications of the model include information evaluation, decision support systems, risk assessment, and various professions like intelligence, research, journalism, and data analysis.
- Professions like intelligence analysts, counterintelligence specialists, researchers, journalists, and data scientists are highlighted as being relevant in applying the 'Trustworthiness' equation.
- The article explores further applications such as evaluating AI-generated content, social media moderation, scientific research assessment, financial risk evaluation, and healthcare information assessment.
- Challenges in quantifying subjectivity, addressing noise, bias, manipulation, and the dynamic nature of truth are discussed within the context of a high-stakes trial scenario.
- The 'Trustworthiness' equation serves as a structured framework for analyzing information reliability and decision-making processes in complex scenarios.
- The application of the model in analyzing witness testimonies and evidence in a high-stakes trial scenario demonstrates its practicality in assessing the reliability of information.
- Various professions such as intelligence analysts, counterintelligence specialists, researchers, journalists, and data scientists play key roles in evaluating information trustworthiness and mitigating bias and noise.
- The 'Trustworthiness' equation provides a systematic approach to analyze different sources of knowledge and to evaluate the credibility and reliability of information in complex situations.
Read Full Article
8 Likes
Medium
2M
87
When ChatGPT Flipped the Script on Language Research
- The transformer model introduced in 2017 initially seemed like a clever hack to many researchers in the language domain.
- The arrival of Google's BERT model in 2018, built on transformers, revolutionized language tasks and sparked a surge of interest in 'BERTology.'
- The launch of OpenAI's GPT-3 in 2020, significantly larger than its predecessor, amazed researchers with its capabilities and scale.
- The field of NLP started dividing between those emphasizing scaling models and others raising concerns about ethics and neglected languages.
- The introduction of ChatGPT in 2022 had a seismic impact, erasing entire research areas and leading to shifts in focus for many researchers.
- Some researchers felt disillusioned by the corporate atmosphere surrounding GPT-3 and the benchmark-driven culture.
- The evolving landscape of NLP prompted varied reactions, with some embracing the advancements while others emphasized the limitations of large language models.
- By 2024, NLP was undergoing a period of consolidation and adaptation, with researchers grappling with the implications of money and corporate involvement.
- The emergence of LLMs led to a flood of attention and scrutiny, propelling researchers like Ellie Pavlick into the limelight.
- The future of NLP remains uncertain, with debates on whether the field is experiencing a paradigm shift or simply an evolution of existing ideas.
Read Full Article
5 Likes
Medium
2M
381
Image Credit: Medium
Eightomic Search: A Fast, New Binary Search Algorithm
- Eightomic Search is a new fast sorted-list search algorithm developed to simplify the selection process of an optimal Binary Search algorithm variant for each implementation.
- It is faster than Binary Search, Exponential Search, and Fibonacci Search while approaching the speed of Interpolation Search, making it ideal for use in situations focusing on space and time complexity guarantees.
- The algorithm design principles of Eightomic Search, such as optimizing the search process and preventing array access inefficiencies, contribute to its speed and efficiency in various scenarios.
- This innovative Binary Search optimization, developed by Eightomic, stands out as a notable improvement over traditional Binary Search algorithms, providing significant speed benefits across different data types and distributions.
Read Full Article
22 Likes
Minis
2y
1.3k
Image Credit: Minis
Salaries of these IT professionals in India are doubled upon switching jobs
- Companies in India and worldwide are doubling salaries for AI professionals, creating a bidding war for tech talent.
- AI and data science specialists in India are seeing pay hikes of 35% to 50% each time they switch jobs, due to an "insatiable need for talent" in the tech industry.
- The rush for AI talent is outstripping supply in India, as recruiters from tech giants like Google and Baidu Inc. and companies in other industries are doubling salaries and offering extravagant sign-on bonuses to lure top talent.
- The shortage of data scientists, machine-learning specialists and skilled engineers in India is pushing salaries to new heights, with recruitment stories verging on the absurd.
Read Full Article
31 Likes
For uninterrupted reading, download the app