Exploring Meta’s AI Endeavours: From Personas to Advantage+ & More
Meta’s main source of revenue is advertising, and its straightforward integration of AI across its apps positions it well to capitalise on AI
Council Post: Towards An Ethical Tech Revolution: Building Responsible AI Practices
A responsible AI framework enables companies to track and mitigate bias and create transparent and explainable AI models, prevent misuse and adverse effects of AI, determine who to be held responsible if something goes wrong, and ensure compliance with security, privacy, and associated regulations.
Guide To AI Explainability 360: An Open Source Toolkit By IBM
In our previous article, we detailed out the need for Trusted AI and discussed one of IBM Research Trusted AI toolkit called AIF360. I recommend you to read this article first for better understanding. In this article, we are going to discuss about AI Explainability 360 toolkit. The growing interactions of the world with AI […]
Top Third-party Initiatives To Ensure Algorithmic FATE For Enterprises
Private enterprises use several algorithms for decision making on a day-to-day basis. Many of them have also realised or have been made aware of the consequences of using algorithms that might be biased towards individuals or sections of society. To avoid such incidents from occurring again, many tech companies are taking steps to ensure FATE […]
What Makes Explainable AI So Difficult
Explainable AI refers to methods and techniques in the application of artificial intelligence such that the results of the solution can be understood by human experts. It contrasts with the concept of the “black box” in machine learning and enables transparency. The need for transparency could be seen in the increased interest of the researchers. […]
8 Explainable AI Frameworks Driving A New Paradigm For Transparency In AI
Due to the ambiguity in Deep Learning solutions, there has been a lot of talk about how to make explainability inclusive of an ML pipeline. Explainable AI refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by human experts. It contrasts […]
How Loss Change Allocation For Machine Learning Training Can Aid Transparency
Whether it is the market crash or a wrong diagnosis, the after-effects will be certainly irreversible. Hence, tracking the development of machine learning algorithm throughout its life cycle becomes crucial. Neural network activations have an underlying compositional, combinatorial structure. Visualising the behaviour of neural networks has been of great interest lately for two reasons — […]
How IoT And Blockchain Can Revolutionise The Manufacturing Industry
IoT has had a lot of applications in different sectors in India. Blockchain too has emerged as an important technology. The manufacturing sector has a lot of potential when integrated with these technologies of IoT and Blockchain. Emerging Use Cases For Blockchain In Manufacturing 1. Improves track and trace accuracy: Combining IoT’s real-time monitoring support […]
9 Reasons Why PyTorch Will Become Your Favourite Deep Learning Tool
Deep learning education and tools are becoming more and more democratic each day. There are only a few major deep learning frameworks; and among them, PyTorch is emerging as a winner. PyTorch is an open-source machine learning library inspired by Torch. It has primarily been developed by Facebook‘s artificial intelligence research group, and Uber‘s Pyro […]
Will The Latest IBM Proposal For Supplier’s Declaration Improve Transparency in AI Algorithms?
Deep learning has had enormous impact on the fields of computer vision, natural language and many other fields. But deep learning models have also been plagued with unexplainability and lack of transparency. The black box nature of DL models is the chief cause for non-interpretability. Now, to overcome these shortcomings, researchers are focusing on ‘Explainable […]
