Analytics Vidhya
FEBRUARY 24, 2024
Stability AI has announced the arrival of Stable Diffusion 3, the latest advancement in AI imagery technology. This new model promises significant improvements in text-to-image generation, boasting enhanced performance and quality. As the anticipation builds around Stable Diffusion 3, let’s delve into the key features and advancements of this innovative AI model.
Eugene Yan
FEBRUARY 24, 2024
How it differs from unit testing typical software and some guidelines
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Analytics Vidhya
FEBRUARY 24, 2024
Google has been a frontrunner in AI research, contributing significantly to the open-source community with transformative technologies like TensorFlow, BERT, T5, JAX, AlphaFold, and AlphaCode. Continuing this legacy, Google has introduced Gemma, an LLM built for responsible AI development, leveraging the same research and technology that powered the Gini models.
Marktechpost
FEBRUARY 24, 2024
The utility of large language models (LLMs) has been increasingly recognized, demonstrating remarkable capabilities in processing and interpreting vast datasets. These models have been instrumental in various tasks, from facilitating clinical trial matches to enabling sophisticated biomedical question-answering. A significant challenge they face is the production of plausible yet inaccurate responses, a phenomenon often attributed to the models’ inability to consult verified sources of inf
Speaker: John Mansour
If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.
Towards AI
FEBRUARY 24, 2024
Last Updated on February 24, 2024 by Editorial Team Author(s): Dr. Leon Eversberg Originally published on Towards AI. Compute-optimal large language models according to the Chinchilla paperThe evolution of GPT’s number of parameters over time. Large Language Models (LLMs) have grown rapidly in size over the past few years. As shown in the graph above, GPT-1 was released in 2018 with 117 million parameters.
Artificial Intelligence Zone brings together the best content for AI and ML professionals from the widest variety of thought leaders.
Towards AI
FEBRUARY 24, 2024
Last Updated on February 24, 2024 by Editorial Team Author(s): Leo Tisljaric, PhD Originally published on Towards AI. From LLM-based machine translations to text generation fine-tuning, find all the theory, examples, and code in one place.Working on AI (Image by: Author) In one of his recent interviews, famous AI scientist Yann LeCun said that we do not have to seek general intelligence because even humans do not have it as „machines“ that have very specific and limited knowledge and skills.
Marktechpost
FEBRUARY 24, 2024
In machine learning, finding the perfect settings for a model to work at its best can be like looking for a needle in a haystack. This process, known as hyperparameter optimization, involves tweaking the settings that govern how the model learns. It’s crucial because the right combination can significantly improve a model’s accuracy and efficiency.
Salmon Run
FEBRUARY 24, 2024
I got into Natural Language Processing (NLP) and Machine Learning (ML) through Search. And this led me into Generative AI (GenAI), which led me back to Search via Retrieval Augmented Generation (RAG). RAG started out relatively simple -- take a query, generate search results, use search results as context for a Large Language Model (LLM) to generate an abstractive summary of the results.
Marktechpost
FEBRUARY 24, 2024
According to recent studies, a policy’s depiction can significantly affect learning performance. Policy representations such as feed-forward neural networks, energy-based models, and diffusion have all been investigated in earlier research. A recent study by Carnegie Mellon University and Peking University researchers proposes producing actions for deep reinforcement and imitation learning using high-dimensional sensory data (images/point clouds) and differentiable trajectory optimization
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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?
Mlearning.ai
FEBRUARY 24, 2024
Discover Writesonic: Your Creative Companion in 2024! Explore the Best AI Writing Software with Our Review. Unleash Your Imagination Today! Source: elegantthemes.com Hey there, fellow content creators, bloggers, and all you marvelous minds out there in the digital cosmos! Are you on the quest for the ultimate AI writing tool that’ll transform your creative process into a breeze?
Marktechpost
FEBRUARY 24, 2024
Cardiac Magnetic Resonance Imaging (CMRI) segmentation plays a crucial role in diagnosing cardiovascular diseases, particularly ischemic heart conditions, which are a leading cause of global mortality. While CMRI offers precise imaging of anatomical regions with minimal risk, segmentation methods primarily focus on short-axis (SAX) views, leaving long-axis (LAX) views comparatively understudied.
Mlearning.ai
FEBRUARY 24, 2024
The word “ethics” has its origins in the Greek word “ethos”, referring to the guiding principles and atmosphere that shape a group’s… Continue reading on MLearning.
Marktechpost
FEBRUARY 24, 2024
MoD-SLAM is a state-of-the-art method for Simultaneous Localization And Mapping (SLAM) systems. In SLAM systems, it is challenging to achieve real-time, accurate, and scalable dense mapping. To address these challenges, researchers have introduced a novel method focusing on unbounded scenes using only RGB images. Existing neural SLAM methods often rely on RGB-D input which leads to inaccurate scale reconstruction or scale drift in large scenes.
Speaker: Scott Sehlhorst
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
Mlearning.ai
FEBRUARY 24, 2024
I bet you didn't know these! Continue reading on MLearning.
Marktechpost
FEBRUARY 24, 2024
Underwater image processing combined with machine learning offers significant potential for enhancing the capabilities of underwater robots across various marine exploration tasks. Image segmentation, a key aspect of machine vision, is crucial for identifying and isolating objects of interest within underwater images. Traditional segmentation methods, such as threshold-based and morphology-based algorithms, have been employed but need help accurately delineating objects in the complex underwater
Unite.AI
FEBRUARY 24, 2024
In today's rapidly evolving cloud landscape, reducing cloud costs while enhancing application performance has become a critical priority for both established enterprises and fast-growing digital native businesses. The “ State of Cloud Optimization 2024 ” report from Granulate, an Intel Company, is a crucial resource in today's cloud-centric IT environment.
Marktechpost
FEBRUARY 24, 2024
The task of view synthesis is essential in both computer vision and graphics, enabling the re-rendering of scenes from various viewpoints akin to the human eye. This capability is vital for everyday tasks and fosters creativity by allowing the envisioning and crafting of immersive objects with depth and perspective. Researchers at Dyson Robotics Lab aim to address the challenge of scalable view synthesis by considering two key observations.
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Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.
Mlearning.ai
FEBRUARY 24, 2024
The final revisions are underway and the EU Parliament is expected to adopt the new EU AI Act in the coming weeks. I took a look at the proposals that will regulate the entire field of machine learning in the EU in the future: Which companies and players will be affected? Which AI technologies will be regulated and how? How will generative AI be dealt with?
Marktechpost
FEBRUARY 24, 2024
A significant challenge with question-answering (QA) systems in Natural Language Processing (NLP) is their performance in scenarios involving extensive collections of documents that are structurally similar or ‘indistinguishable.’ Traditional models often need help to retrieve accurate information from such massive, homogeneous datasets, leading to issues in the precision and relevance of the responses.
Mlearning.ai
FEBRUARY 24, 2024
Most of you have already captured some pictures of a board at school. Either because you were too lazy to write down what was written on the board, or you were in a hurry and had to take quick notes. Most of those pictures will obviously end up in the forgotten realm. Don’t worry, I know it’s still the laziness keeping us from checking them. In our mind it would be easier if we could get a summary of the content within the image.
Bugra Akyildiz
FEBRUARY 24, 2024
Articles Microsoft Research introduces Phi-2 , a surprisingly powerful small language model (SLM) with only 2.7 billion parameters. Despite its size, Phi-2 surpasses much larger models on various benchmarks, highlighting the potential of SLMs. Key Differences: Superior Performance: Phi-2 outperforms larger models (7B and 13B parameters) on language reasoning and coding tasks, even without alignment or fine-tuning.
Speaker: Anne Steiner and David Laribee
As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.
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