Actable AI

Causal Discovery is now easier than ever. Causal Discovery helps our users discover potential causal relationships from observational data automatically.

Thank UKT News for featuring us ukt.news/how-is-causal-…

Bring your data analytics to the next level by installing our add-on and start using them with only $16/month here 🤩: lnkd.in/e4gBQWjD #datascience #dataanalytics #machinelearning #causalml #automl #ai

🙌Data analysts & data scientists: have you ever dreamed of having all of these statistical, AutoML & CausalML tools right next to your data? All of these descriptive and latest advanced ML tools are now avaialble in Google Sheets with our Actable AI add-on.

Try it again today and let us know it feels. If you are not already signed up, install it here workspace.google.com/.../app/actabl… * We have now added: Association Rules, ANOVA, Bayesian Linear Regression, Chi-Squared Test, T-Test and Whitney/Wilcoxon Tests.

2. Additional Machine Learning and Statistics tools* for more insights and recommendation models. 3. Hyperparameter search for Regression, Classification, Timeseries forecasting and Causal Inference. 4. New modern UI.

📷📷We are happy to announce big major updates to our Google Sheets add-on: 1. New pricing: $16/month for Basic, $40/month for Pro and $200/month for Preminum (more details here actable.ai/pricing).

Please check out our new blog post for business leaders on why you should consider Causal AI for their data analytics. actable.ai/blog/harnessin…

Checking out our CEO's interview with VnEconomy Digital about our feature in AlbionVC's AI/data map and ideas on how to boost AI startups in Vietnam vneconomy.vn/founder-actabl…

Actable AI is very proud be featured on the @AlbionVC Data & AI market map as one of the leading data analytics companies in Europe.🔥 It's great to see our world-class innovative solution is recognized by one of the leading VCs in Europe. albion.vc/spotlight/deep…

Generative AI vs Causal AI: when to use it? As Generative AI is attracting lots of attention, there is much hype around it. Till today I have overheard at least a couple of consultants to consult clients to use Generative AI for anything from customer-service chatbots to better demand forecast. While Gen AI is great for certain tasks such as synthesizing text, it should not be an automatic choice for everything. As of today, Gen AI is trained to generate text and images, their strengths are in information retrieval, content creation, language understanding and generation. Its strengths make Gen AI most suitable for automation such as customer-service chatbots, code co-pilots or personal assistants. To make better forecast or derive business decisions from data, Gen AI is not the most suitable technology. There have been studies showing LLMs are as good as a random guesser in Causal Inference (estimate causal effect from data). The reasoning ability of LLMs is also highly questionable. Though minor tasks can be automated with Gen AI, the accuracy of AI applications in more complex real-world scenarios cannot be relied upon. Meanwhile Causal AI, an emerging field of AI, is designed to solve these problems by incorporating cause-effect reasoning. The technology helps you uncover causal relationships and estimate causal effects from observational data with AI. This enables you to make better business decisions with AI-assisted data analytics and causal predictions. An example is allowing ridesharing companies to predict customer demand more accurately by understanding and modelling the causal relationship between different factors, such as weather, festivals and promotional campaigns. However there is an area where Generative AI may have an advantage over Causal AI in decision intelligence is simulation. An example is Wayve, a self-driving car startup in the UK, has used Gen AI to generate simulations of future scenarios conditioned by natural-language instructions. If one has a lot of data and compute, Gen AI can be potentially trained to generate highly-complex predictive scenarios. In conclusion, while Gen AI should not be the automatic choice for every use case, it has its strengths in certain tasks such as synthesis of text, images, automation and simulation. Even though Gen AI excels in these tasks, for tasks such as forecasting and decision-making, Causal AI is likely to be the more suitable and accurate choice. #AI #GenerativeAI #CausalAI #DecisionIntelligence #DataScience

12,500th analysis was created on @ActableAi today👌

Some good discussion on Causal ML's real-life impact on r/datascience reddit.com/r/datascience/…

Nice blog post on how Causal AI can help speed up Randomized Clincal Trials and foster bringing new treatments to the right patents. #CausalAI #DataScience #MachineLearing #AI linkedin.com/pulse/beyond-c…

Exactly why we never bother adding SMOTE in @ActableAi

+12K installations and going strong 🚀🚀🚀

And projection algorithms: 1. LDA 2. T-sne 3. UMAP Please check out the demo below for more information. #datascience #machinelearning #dataanalytics #ai #clustering

Pleased to announce that we now support many more clustering algorithms in our segmentation including: 1. Affinity Propagation 2. Agglomerative Clustering 3. DBSCAN 4. Deep Embedding Clustering 5. K-Means 6. Spectral Clustering youtu.be/YexvW8Y7SNw

You can check out more documentation here: #pdp-ice" target="_blank" rel="nofollow noopener">docs.actable.ai/regression.htm… #datascience #interpretability #machinelearning #ai #modelauditing #explanability

Meanwhile ICE helps auditors gain insights into the model's behavior at the individual level and ensure that the model's predictions align with the intended objectives and ethical considerations.

Model auditing: Partial Dependence Plot (PDP) and Individual Conditional Expectation (ICE) plot are now added for Regression and Classification. youtu.be/dlkkNEnqn8w