Quelles sont les meilleures façons d’intégrer l’IA dans la programmation statistique ?

Incorporating AI into stat programming can enhance the ability to analyze and interpret data. Some of the best ways to do so: - Use AI for data preprocessing tasks, including data cleaning, imputation, and feature engineering. AI can help automate and improve these processes. - Utilize machine learning libraries to implement AI algorithms for predictive modeling, classification, regression, and clustering. Some popular names are such as Scikit-Learn (Python), Caret (R), and TensorFlow. - Use reinforcement learning algorithms to solve problems that involve sequential decision-making, such as optimization, control systems, or game theory. Key is to choose the right AI techniques and tools that align with specific analytical objectives.

A real-world example from my experience is when we used AI to analyze customer behaviour data for a retail client. Traditional statistical methods were falling short in deciphering the vast amount of data. However, by employing machine learning algorithms, we were able to identify hidden purchasing patterns which greatly informed the client's marketing strategies. Besides, AI's power in automating mundane data pre-processing tasks was a time-saver, allowing us to focus on more complex analyses. This fusion of AI and statistical programming not only enriched our data analysis toolkit but also led to more insightful and actionable findings for our client.

In my experience, Incorporating Machine Learning technique during feature selection and engineering processes is something that I do quite regularly. Statistical programming/algorithm are still used widely especially for regulatory modelling. Assessing best run of features on larger data is tricky - Think stepwise on 30mn records and 500 columns. Now , using something like XGboost and LightGBM and getting feature importance really helps in churning many variables and thus helping in building optimal model. Think of LOFO as an alternative during feature selection or even looking at feature interactions can help in finding/choosing/creating best feature set.

The idea of AI in statistical programming also allows for the exploration of the best way to analyze data and communicate data reports to decision makers. If an AI algorithm were created to optimize data reports, it could be done by categorizing problem statements, creating an algorithm that outputs multiple data reports for each problem statement, and selecting the optimal report(s) that decision makers can understand for each problem statement without losing the integrity of the data. (The algorithms input variables can be changed to cater to the decision maker’s career field’s visual preferences and common problem statements.)

"You get what you measure" no matter what, be it a new or an upgrade of an existing Machine Learning pipeline, or integration with a large or small Language Model, we need best practices and recipes for assessing their impact on the business outcomes. On the other hand, in my opinion Feature Engineering cannot be imagined without statistical programming. This way, I see statistical programming entangled with ML and AI.

The integration of AI in statistical programming is a game changer. Tools like TensorFlow and PyTorch offer robust platforms for building and training AI models, while languages like R and Python, with libraries like SciKit-Learn, provide a solid foundation for statistical analysis. For instance, in a recent project on customer behaviour analysis, utilizing PyTorch alongside R's statistical prowess led to more accurate predictions. Frameworks like H2O.ai are also ground-breaking, offering an array of algorithms for various data tasks. These tools are not just enhancing statistical programming, but revolutionizing the way we derive insights from data.

There are several ways to incorporate AI into statistical programming. Here are some ideas: - Deep learning: Deep learning is a form of artificial intelligence that extracts patterns from mountains of data to make predictions. It has been widely used in statistical programming to improve the accuracy of predictions. - Exploratory data analysis: Exploratory data analysis is a technique used to analyze and summarize datasets. It has been hugely influential in the field of statistics. - Automation of routine activities, optimization of processes, and the provision of predictive insights are all ways in which AI and ML may boost productivity. Through enhanced individualization and real-time data analysis, it can also boost productivity.

In the realm of statistical programming, tools like LIME and SHAP are indispensable. Picture this: applying advanced statistical techniques yielded a surprising result. LIME stepped in, breaking down complex model decisions at a local level. Meanwhile, SHAP quantified global feature contributions, enhancing overall interpretability. In my eyes, people often overlook the explainability aspect in statistics—LIME and SHAP address this blind spot, offering crucial insights into intricate models, fostering trust, and aligning statistical programming with transparency standards

Choosing the right framework is a matter of different elements: - If the data has to be processed in real time or in batch - If there are any GPUs available or not - The composition of the data - If the data presents a linear pattern - etc. I usually spend 40% of my total working time in analyzing data and the patterns that can be spot within the same. Some tools I use are: - Seaborn to visualize data - scipy to perform linear analysis Models can be chosen among a variety of libraries, such as: - Tensorflow, JAX, Pytorch, for deeplearning modeling - SKlearn for normal Machine Learning

The first and the foremost aspect of any choice of framework should come from the use-case. What is that you are trying to do? And then choose a tool / framework that is the right fit. Some question you might want to ask: - what kind of a task is it? Supervised, Semi Supervised, Unsupervised, RL? - how is your solution going to affect user experience? - what compute restrictions you might have? - how do you test your hypothesis? - how do you monitor your models in production for drift, bias and trustworthiness. - what data you have at hand and how much of it is actually useful? These are some questions, but the summary is that, choosing a framework is about first being able to understand the problem and then making choices accordingly

Based on my experience, I strongly advocate learning the mathematical principles behind various algorithms and AI techniques. Acquiring this knowledge forms a solid foundation for your journey. It enables you to effectively apply these concepts along with diverse frameworks or libraries, empowering you to make informed decisions about the most optimal solutions. Additionally, I recommend actively practicing and experimenting with each concept as you learn from various resources like books, tutorials, or courses. Engaging in practical exercises significantly enhances your skills to the next level.

Flipping the question around, AI (at least neural-nets) is essentially 3 mathematical disciplines: - probability/statistics (generating a prob distribution across all potential outputs) - linear algebra (matrix operations) - calculus (back-prop=chain rule)

This seems to be an ill posed question as statistical programming is a prerequisite to AI development. This question is equivalent to: “How do I improve my calculus skills for algebra 1?”. It’s interesting the number of “expert responses” that seem to lack a fundamental understanding of their supposed expertise. Regardless, reversing this question yields the much more interesting problem of establishing a statistical base for AI. While 90% of AI development requires very little, if any, hard math, having a basic intuition around the statistical properties of your data can be invaluable for identifying bottlenecks and optimizing performance. A basic understanding of Bayesian statistics is likely sufficient for most use cases!

To improve your AI skills you must understand exactly what AI and its interrelated disciplines like Machine Learning and Deep learning are. DL and ML are both subsets of AI. AI is a broad field that refers to the use of technologies to build machines and computers that can mimic cognitive functions associated with human intelligence. ML is a subset of AI that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to analyze enormous amounts of data, learn from the insights, and then make informed decisions. DL is a subset of ML that uses artificial neural networks with multiple layers to analyze complex patterns and relationships in data.

Well, there's no avoiding the hands-on experience of 'doing it.' In practice, true learning occurs when tackling a problem or constructing an AI-led use case. It's in these moments that all the nuances of both the problem and the solution reveal themselves

Hyperparameter tuning is another great way to do this :It helps Optimize model parameters to achieve the best results using techniques like grid search or Bayesian optimization.

It is import to consider privacy, bias and trustworthiness of our models when deployed to our customers. Having robust controls to scrutinise these aspects and iteratively improve, help make a positive difference to peoples life’s using the power of AI

Important factors combining AI with statistical programming: 1.Quality Data 2. Algorithm Selection 3.Feature Engineering 4.Model Training and Tuning 5.Interpretability 6.Validation and Testing 7.Scalability Statistics is use of data analysis, visualisation, research. When it will be combined with AI then it help human intelligence to get profound outcomes and help them in decision making with considering above factors. AI is good with only quality data. If you are training model with bad data, AI produce bad model so don’t expect that you will predict decision with much accuracy. In telecom for predicting customer churn statistical analysis is important and AI help in advancing the model and help to identify churning customers.