Augmented analytics and explainable AI (XAI) are among the top data and analytics technology trends for 2019, according to many reports. In this article, we
Chinmaya Jena highlights that the problem with LLM is that there is no differentiation between the data plane and the control plane.
Through the implementation of advanced data management methodologies, resilient data observability solutions, and cutting-edge AI frameworks, Course5 is spearheading the evolution of AI-powered decision-making within the industry.
Despite its supremacy, Google has competitors providing alternatives that boast the same use case with enhanced safety and privacy.
“What we’ve seen with generative AI is the ability for it to seemingly reason about test scenarios that that could be interesting but may have been overlooked,” said Rangarajan Vasudevan, CDO of Lentra
watsonx.governance is a toolkit for organisations, addressing risks, boosting transparency, and preparing for AI regulations
One of the areas where healthcare companies are heavily investing is disease diagnosis.
Process innovation in the age of algorithms.
IBM’s foundational models are being trained not just on language, but on a variety of modalities, including code, time-series data, tabular data, geospatial data, and IT events data
Wipro will launch a GenAI Seed Accelerator programme, which will provide select GenAI-focused startups with the training needed to become enterprise-ready
The integration of LLMs and Neo4j’s graph database technology improves efficiency and accuracy.
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.
Everyone now compares AI to atomic bomb and talks about AI ethics.
Visionary Geoffrey Hinton recently left Google to speak out about the dangers of AI
AWS’s essential offering, Amazon Braket, enables developers and researchers to test their quantum computing algorithms on quantum simulators and quantum hardware
“Quantum generative AI has the potential to revolutionise drug and material design by helping researchers to generate new and innovative compounds that could not be discovered using traditional methods.”
Oliver said the problem is AI is “stupid in ways we can’t always predict”
While everyone focuses on model manufacturing, the right product teams have started emphasising the fundamentals of good AI Solutions. XAI is the 101 feature to achieve it. However, the vision of achieving trustworthy AI is incomplete without Explainability. The idea that Explainability will provide insights into understanding model behaviours is, however, currently only serving the needs of AI experts.
Google’s grant to IIT-M focuses on training natural language processing models to mitigate gender bias. This will be done through the establishment of a multi-disciplinary centre for responsible AI
The year 2022 was dedicated to large language models and generative art, let’s see what’s in the AI goody bag for 2023
Current explainability techniques were only able to produce “broad descriptions of how the AI system works in a general sense” but when asked to justify how individual decisions were made, the explanations were “unreliable and superficial.”
The acquisition is expected to strengthen the analytics firm’s end-to-end data analytics (DA) portfolio.
“Knowing your data is really important, whatever AI/ML problem that you solve, one should know how complex your data is” says Dr Joshi, CEO, KroopAI
We help AI and ML teams identify where their models are underperforming, the potential issues, and how they can be improved: Censius founder Ayush Patel
Financial details of the deal were not disclosed
Historically, AI and machine learning have had the reputation of being a black box.
Frankly, calling this engineering is offending to engineers. It’s chaos alchemy.
TensorFlow Federated (TFF) is an open-source framework for decentralised machine learning.
To reduce the chances of biases creeping into our AI, we first define and buttonhole the business problem we mean to solve, keeping our end-users in mind, and then configure our data collection methods to make room for diverse, valid opinions as they keep the AI model limber and flexible.
We use the CRISP-DM methodology to ensure each data science problem is solved holistically and meets business expectations and standards.
Join the forefront of data innovation at the Data Engineering Summit 2024 where industry leaders redefine technology 8217 s future
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