#data analytics & ai

[victusinveritas]

[michaelmusselman]

[turns-out-its-adhd]

AI exists and there's nothing any of us can do to change that.

If you have concerns about how AI is being/will be used the solution is not to abstain - it's to get involved.

Learn about it, practice utilising AI tools, understand it. Ignorance will not protect you, and putting your fingers in your ears going 'lalalala AI doesn't exist I don't acknowledge it' won't stop it from affecting your life.

The more the general population fears and misunderstands this technology, the less equipped they will be to resist its influence.

[xlsdesignt]

what u think, to much colour, or less?

https://sdesignt.threadless.com/

[walkawaytall]

Every time I hear about someone using ChatGPT or another LLM to generate code for their job, I am baffled because I hate inheriting code from another person. Even someone who writes clean, performant code will have their own style or choice of solution that is different than mine, so I have to make a mental adjustment when I'm working on something I inherited from someone else, and half the time, I end up reworking a large chunk of what I've been handed.

So, I'm imagining that situation, but also I can't even ask the entity generating the code why they did something, and I just simply don't see the appeal.

I've heard people say they mainly use it for simple-but-tedious code writing, and maybe I'm just built different, but if I find myself writing something over and over again, I...save a template. (I'm being sarcastic. I am not brilliant or unusual for doing this. I just don't know how using ChatGPT for simple things is any less tedious than using a template.) Or, depending on what program I'm using, there are built-in tools and functions like macros or stored procedures that cut down on duplicating effort.

I also find the mere idea of having to explain to a program what I want to be more time-consuming and annoying than just writing it myself, so it might just be that it's not for me. But I also don't fully trust the way the code is being generated. So...

[jcmarchi]

Non-fiction books that explore AI's impact on society  - AI News

New Post has been published on https://thedigitalinsider.com/non-fiction-books-that-explore-ais-impact-on-society-ai-news/

Non-fiction books that explore AI's impact on society  - AI News

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Artificial Intelligence (AI) is code or technologies that perform complex calculations, an area that encompasses simulations, data processing and analytics.

AI has increasingly grown in importance, becoming a game changer in many industries, including healthcare, education and finance. The use of AI has been proven to double levels of effectiveness, efficiency and accuracy in many processes, and reduced cost in different market sectors. 

AI’s impact is being felt across the globe, so, it is important we understand the effects of AI on society and our daily lives. 

Better understanding of AI and all that it does and can mean can be gained from well-researched AI books.

Books on AI provide insights into the use and applications of AI. They describe the advancement of AI since its inception and how it has shaped society so far. In this article, we will be examining recommended best books on AI that focus on the societal implications.

For those who don’t have time to read entire books, book summary apps like Headway will be of help.

Book 1: “Superintelligence: Paths, Dangers, Strategies” by Nick Bostrom

Nick Bostrom is a Swedish philosopher with a background in computational neuroscience, logic and AI safety. 

In his book, Superintelligence, he talks about how AI  can surpass our current definitions of intelligence and the possibilities that might ensue.

Bostrom also talks about the possible risks to humanity if superintelligence is not managed properly, stating AI can easily become a threat to the entire human race if we exercise no control over the technology. 

Bostrom offers strategies that might curb existential risks, talks about how Al can be aligned with human values to reduce those risks and suggests teaching AI human values.

Superintelligence is recommended for anyone who is interested in knowing and understanding the implications of AI on humanity’s future.

Book 2: “AI Superpowers: China, Silicon Valley, and the New World Order” by Kai-Fu Lee

AI expert Kai-Fu Lee’s book, AI Superpowers: China, Silicon Valley, and the New World Order, examines the AI revolution and its impact so far, focusing on China and the USA. 

He concentrates on the competition between these two countries in AI and the various contributions to the advancement of the technology made by each. He highlights China’s advantage, thanks in part to its larger population. 

China’s significant investment so far in AI is discussed, and its chances of becoming a global leader in AI. Lee believes that cooperation between the countries will help shape the future of global power dynamics and therefore the economic development of the world.

In thes book, Lee states AI has the ability to transform economies by creating new job opportunities with massive impact on all sectors. 

If you are interested in knowing the geo-political and economic impacts of AI, this is one of the best books out there. 

Book 3: “Life 3.0: Being Human in the Age of Artificial Intelligence” by Max Tegmark

Max Tegmark’s Life 3.0 explores the concept of humans living in a world that is heavily influenced by AI. In the book, he talks about the concept of Life 3.0, a future where human existence and society will be shaped by AI. It focuses on many aspects of humanity including identity and creativity. 

Tegmark envisions a time where AI has the ability to reshape human existence. He also emphasises the need to follow ethical principles to ensure the safety and preservation of human life. 

Life 3.0 is a thought-provoking book that challenges readers to think deeply about the choices humanity may face as we progress into the AI era. 

It’s one of the best books to read if you are interested in the ethical and philosophical discussions surrounding AI.

Book 4: “The Fourth Industrial Revolution” by Klaus Schwab

Klaus Martin Schwab is a German economist, mechanical engineer and founder of the World Economic Forum (WEF). He argues that machines are becoming smarter with every advance in technology and supports his arguments with evidence from previous revolutions in thinking and industry.

He explains that the current age – the fourth industrial revolution – is building on the third: with far-reaching consequences.

He states use of AI in technological advancement is crucial and that cybernetics can be used by AIs to change and shape the technological advances coming down the line towards us all.

This book is perfect if you are interested in AI-driven advancements in the fields of digital and technological growth. With this book, the role AI will play in the next phases of technological advancement will be better understood.

Book 5: “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy” by Cathy O’Neil

Cathy O’Neil’s book emphasises the harm that defective mathematical algorithms cause in judging human behaviour and character. The continual use of maths algorithms promotes harmful results and creates inequality.

An example given in  the book is of research that proved bias in voting choices caused by results from different search engines.

Similar examination is given to research that focused Facebook, where, by making newsfeeds appear on users’ timelines, political preferences could be affected.

This book is best suited for readers who want to adventure in the darker sides of AI that wouldn’t regularly be seen in mainstream news outlets.

Book 6: “The Age of Em: Work, Love, and Life when Robots Rule the Earth” by Robin Hanson

An associate professor of economics at George Mason University and a former researcher at the Future of Humanity Institute of Oxford University, Robin Hanson paints an imaginative picture of emulated human brains designed for robots. What if humans copied or “emulated” their brains and emotions and gave them to robots?

He argues that humans who become “Ems” (emulations) will become more dominant in the future workplace because of their higher productivity.

An intriguing book for fans of technology and those who love intelligent predictions of possible futures.

Book 7: “Architects of Intelligence: The truth about AI from the people building it” by Martin Ford

This book was drawn from interviews with AI experts and examines the struggles and possibilities of AI-driven industry.

If you want insights from people actively shaping the world, this book is right for you!

CONCLUSION

These books all have their unique perspectives but all point to one thing – the advantages of AI of today will have significant societal and technological impact. These books will give the reader glimpses into possible futures, with the effects of AI becoming more apparent over time.

For better insight into all aspects of AI, these books are the boosts you need to expand your knowledge. AI is advancing quickly, and these authors are some of the most respected in the field. Learn from the best with these choice reads.

[careerlaunchpad]

Cognizance IIT Roorkee Internship and Training Program

Registration Link : https://forms.gle/E2cHdnjyzYytKxC39

[pandeypankaj]

How do I learn Python in depth?

Improving Your Python Skills

  Writing Python Programs Basics: Practice the basics solidly. 

  Syntax and Semantics: Make sure you are very strong in variables, data types, control flow, functions, and object-oriented programming. 

 Data Structures: Be able to work with lists, tuples, dictionaries, and sets, and know when to use which. 

 Modules and Packages: Study how to import and use built-in and third-party modules. 

Advanced Concepts

Generators and Iterators: Know how to develop efficient iterators and generators for memory-efficient code. 

Decorators: Learn how to dynamically alter functions using decorators. 

Metaclasses: Understand how classes are created and can be customized. 

Context Managers: Understand how contexts work with statements. 

Project Practice 

 Personal Projects: You will work on projects that you want to, whether building a web application, data analysis tool, or a game.

 Contributing to Open Source: Contribute to open-source projects in order to learn from senior developers. Get exposed to real-life code. 

 Online Challenges: Take part in coding challenges on HackerRank, LeetCode, or Project Euler. 

 Learn Various Libraries and Frameworks

 Scientific Computing: NumPy, SciPy, Pandas

 Data Visualization: Matplotlib, Seaborn

 Machine Learning: Scikit-learn, TensorFlow, PyTorch

 Web Development: Django, Flask

Data Analysis: Dask, Airflow

Read Pythonic Code

 Open Source Projects: Study the source code of a few popular Python projects. Go through their best practices and idiomatic Python. 

 Books and Tutorials: Read all the code examples in books and tutorials on Python. 

 Conferences and Workshops

  Attend conferences and workshops that will help you further your skills in Python. PyCon is an annual Python conference that includes talks, workshops, and even networking opportunities. Local meetups will let you connect with other Python developers in your area. 

Learn Continuously

 Follow Blogs and Podcasts: Keep reading blogs and listening to podcasts that will keep you updated with the latest trends and developments taking place within the Python community.

Online Courses: Advanced understanding in Python can be acquired by taking online courses on the subject.

 Try It Yourself: Trying new techniques and libraries expands one's knowledge.

Other Recommendations

 Readable-Clean Code: For code writing, it's essential to follow the style guide in Python, PEP 

Naming your variables and functions as close to their utilization as possible is also recommended.

 Test Your Code: Unit tests will help in establishing the correctness of your code.

 Coding with Others: Doing pair programming and code reviews would provide you with experience from other coders.

 You are not Afraid to Ask for Help: Never hesitate to ask for help when things are beyond your hand-on areas, be it online communities or mentors.

These steps, along with consistent practice, will help you become proficient in Python development and open a wide range of possibilities in your career.

[mvishnukumar]

Can statistics and data science methods make predicting a football game easier?

Hi,

Statistics and data science methods can significantly enhance the ability to predict the outcomes of football games, though they cannot guarantee results due to the inherent unpredictability of sports. Here’s how these methods contribute to improving predictions:

Data Collection and Analysis: 

Collecting and analyzing historical data on football games provides a basis for understanding patterns and trends. This data can include player statistics, team performance metrics, match outcomes, and more. Analyzing this data helps identify factors that influence game results and informs predictive models.

Feature Engineering:

 Feature engineering involves creating and selecting relevant features (variables) that contribute to the prediction of game outcomes. For football, features might include team statistics (e.g., goals scored, possession percentage), player metrics (e.g., player fitness, goals scored), and contextual factors (e.g., home/away games, weather conditions). Effective feature engineering enhances the model’s ability to capture important aspects of the game.

Predictive Modeling: 

Various predictive models can be used to forecast football game outcomes. Common models include:

Logistic Regression: This model estimates the probability of a binary outcome (e.g., win or lose) based on input features.

Random Forest: An ensemble method that builds multiple decision trees and aggregates their predictions. It can handle complex interactions between features and improve accuracy.

Support Vector Machines (SVM): A classification model that finds the optimal hyperplane to separate different classes (e.g., win or lose).

Poisson Regression: Specifically used for predicting the number of goals scored by teams, based on historical goal data.

Machine Learning Algorithms: 

Advanced machine learning algorithms, such as gradient boosting and neural networks, can be employed to enhance predictive accuracy. These algorithms can learn from complex patterns in the data and improve predictions over time.

Simulation and Monte Carlo Methods: 

Simulation techniques and Monte Carlo methods can be used to model the randomness and uncertainty inherent in football games. By simulating many possible outcomes based on historical data and statistical models, predictions can be made with an understanding of the variability in results.

Model Evaluation and Validation: 

Evaluating the performance of predictive models is crucial. Metrics such as accuracy, precision, recall, and F1 score can assess the model’s effectiveness. Cross-validation techniques ensure that the model generalizes well to new, unseen data and avoids overfitting.

Consideration of Uncertainty: 

Football games are influenced by numerous unpredictable factors, such as injuries, referee decisions, and player form. While statistical models can account for many variables, they cannot fully capture the uncertainty and randomness of the game.

Continuous Improvement: 

Predictive models can be continuously improved by incorporating new data, refining features, and adjusting algorithms. Regular updates and iterative improvements help maintain model relevance and accuracy.

In summary, statistics and data science methods can enhance the ability to predict football game outcomes by leveraging historical data, creating relevant features, applying predictive modeling techniques, and continuously refining models. While these methods improve the accuracy of predictions, they cannot eliminate the inherent unpredictability of sports. Combining statistical insights with domain knowledge and expert analysis provides the best approach for making informed predictions.

[pleasantinternetfest]

Data Fragmentation

This is the list of challenges that the businesses face due to data distribution across multiple systems in the digital lending industry

[victusinveritas]

[emerging-jew]

Cloud modernization is out, generative AI is in

[innonurse]

Researchers create AI tool to forecast cancer patients' responses to immunotherapy

- By InnoNurse Staff -

NIH scientists have developed an AI tool that uses routine clinical data to predict cancer patients' responses to immunotherapy, potentially aiding in treatment decisions.

Read more at National Institutes of Health (NIH)

///

Other recent news and insights

New analytical tool enhances comprehension of heritable human traits and diseases (University of Oslo/Medical Xpress)

[newfangled-polusai]

Top 5 Benefits of Low-Code/No-Code BI Solutions

Low-code/no-code Business Intelligence (BI) solutions offer a paradigm shift in analytics, providing organizations with five key benefits. Firstly, rapid development and deployment empower businesses to swiftly adapt to changing needs. Secondly, these solutions enhance collaboration by enabling non-technical users to contribute to BI processes. Thirdly, cost-effectiveness arises from reduced reliance on IT resources and streamlined development cycles. Fourthly, accessibility improves as these platforms democratize data insights, making BI available to a broader audience. Lastly, agility is heightened, allowing organizations to respond promptly to market dynamics. Low-code/no-code BI solutions thus deliver efficiency, collaboration, cost savings, accessibility, and agility in the analytics landscape.

[jcmarchi]

Future-Ready Enterprises: The Crucial Role of Large Vision Models (LVMs)

New Post has been published on https://thedigitalinsider.com/future-ready-enterprises-the-crucial-role-of-large-vision-models-lvms/

Future-Ready Enterprises: The Crucial Role of Large Vision Models (LVMs)

What are Large Vision Models (LVMs)

Over the last few decades, the field of Artificial Intelligence (AI) has experienced rapid growth, resulting in significant changes to various aspects of human society and business operations. AI has proven to be useful in task automation and process optimization, as well as in promoting creativity and innovation. However, as data complexity and diversity continue to increase, there is a growing need for more advanced AI models that can comprehend and handle these challenges effectively. This is where the emergence of Large Vision Models (LVMs) becomes crucial.

LVMs are a new category of AI models specifically designed for analyzing and interpreting visual information, such as images and videos, on a large scale, with impressive accuracy. Unlike traditional computer vision models that rely on manual feature crafting, LVMs leverage deep learning techniques, utilizing extensive datasets to generate authentic and diverse outputs. An outstanding feature of LVMs is their ability to seamlessly integrate visual information with other modalities, such as natural language and audio, enabling a comprehensive understanding and generation of multimodal outputs.

LVMs are defined by their key attributes and capabilities, including their proficiency in advanced image and video processing tasks related to natural language and visual information. This includes tasks like generating captions, descriptions, stories, code, and more. LVMs also exhibit multimodal learning by effectively processing information from various sources, such as text, images, videos, and audio, resulting in outputs across different modalities.

Additionally, LVMs possess adaptability through transfer learning, meaning they can apply knowledge gained from one domain or task to another, with the capability to adapt to new data or scenarios through minimal fine-tuning. Moreover, their real-time decision-making capabilities empower rapid and adaptive responses, supporting interactive applications in gaming, education, and entertainment.

How LVMs Can Boost Enterprise Performance and Innovation?

Adopting LVMs can provide enterprises with powerful and promising technology to navigate the evolving AI discipline, making them more future-ready and competitive. LVMs have the potential to enhance productivity, efficiency, and innovation across various domains and applications. However, it is important to consider the ethical, security, and integration challenges associated with LVMs, which require responsible and careful management.

Moreover, LVMs enable insightful analytics by extracting and synthesizing information from diverse visual data sources, including images, videos, and text. Their capability to generate realistic outputs, such as captions, descriptions, stories, and code based on visual inputs, empowers enterprises to make informed decisions and optimize strategies. The creative potential of LVMs emerges in their ability to develop new business models and opportunities, particularly those using visual data and multimodal capabilities.

Prominent examples of enterprises adopting LVMs for these advantages include Landing AI, a computer vision cloud platform addressing diverse computer vision challenges, and Snowflake, a cloud data platform facilitating LVM deployment through Snowpark Container Services. Additionally, OpenAI, contributes to LVM development with models like GPT-4, CLIP, DALL-E, and OpenAI Codex, capable of handling various tasks involving natural language and visual information.

In the post-pandemic landscape, LVMs offer additional benefits by assisting enterprises in adapting to remote work, online shopping trends, and digital transformation. Whether enabling remote collaboration, enhancing online marketing and sales through personalized recommendations, or contributing to digital health and wellness via telemedicine, LVMs emerge as powerful tools.

Challenges and Considerations for Enterprises in LVM Adoption

While the promises of LVMs are extensive, their adoption is not without challenges and considerations. Ethical implications are significant, covering issues related to bias, transparency, and accountability. Instances of bias in data or outputs can lead to unfair or inaccurate representations, potentially undermining the trust and fairness associated with LVMs. Thus, ensuring transparency in how LVMs operate and the accountability of developers and users for their consequences becomes essential.

Security concerns add another layer of complexity, requiring the protection of sensitive data processed by LVMs and precautions against adversarial attacks. Sensitive information, ranging from health records to financial transactions, demands robust security measures to preserve privacy, integrity, and reliability.

Integration and scalability hurdles pose additional challenges, especially for large enterprises. Ensuring compatibility with existing systems and processes becomes a crucial factor to consider. Enterprises need to explore tools and technologies that facilitate and optimize the integration of LVMs. Container services, cloud platforms, and specialized platforms for computer vision offer solutions to enhance the interoperability, performance, and accessibility of LVMs.

To tackle these challenges, enterprises must adopt best practices and frameworks for responsible LVM use. Prioritizing data quality, establishing governance policies, and complying with relevant regulations are important steps. These measures ensure the validity, consistency, and accountability of LVMs, enhancing their value, performance, and compliance within enterprise settings.

Future Trends and Possibilities for LVMs

With the adoption of digital transformation by enterprises, the domain of LVMs is poised for further evolution. Anticipated advancements in model architectures, training techniques, and application areas will drive LVMs to become more robust, efficient, and versatile. For example, self-supervised learning, which enables LVMs to learn from unlabeled data without human intervention, is expected to gain prominence.

Likewise, transformer models, renowned for their ability to process sequential data using attention mechanisms, are likely to contribute to state-of-the-art outcomes in various tasks. Similarly, Zero-shot learning, allowing LVMs to perform tasks they have not been explicitly trained on, is set to expand their capabilities even further.

Simultaneously, the scope of LVM application areas is expected to widen, encompassing new industries and domains. Medical imaging, in particular, holds promise as an avenue where LVMs could assist in the diagnosis, monitoring, and treatment of various diseases and conditions, including cancer, COVID-19, and Alzheimer’s.

In the e-commerce sector, LVMs are expected to enhance personalization, optimize pricing strategies, and increase conversion rates by analyzing and generating images and videos of products and customers. The entertainment industry also stands to benefit as LVMs contribute to the creation and distribution of captivating and immersive content across movies, games, and music.

To fully utilize the potential of these future trends, enterprises must focus on acquiring and developing the necessary skills and competencies for the adoption and implementation of LVMs. In addition to technical challenges, successfully integrating LVMs into enterprise workflows requires a clear strategic vision, a robust organizational culture, and a capable team. Key skills and competencies include data literacy, which encompasses the ability to understand, analyze, and communicate data.

The Bottom Line

In conclusion, LVMs are effective tools for enterprises, promising transformative impacts on productivity, efficiency, and innovation. Despite challenges, embracing best practices and advanced technologies can overcome hurdles. LVMs are envisioned not just as tools but as pivotal contributors to the next technological era, requiring a thoughtful approach. A practical adoption of LVMs ensures future readiness, acknowledging their evolving role for responsible integration into business processes.