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Explore how Project Euphonia’s open-source tools empower developers to build personalized audio tools and fine-tune open-weight models for diverse speech patterns, advancing speech tech for everyone. #GAAD → g.co/euphonia
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Today on the blog, we share a comprehensive evaluation of transformer models’ graph reasoning capabilities, shedding light on their effectiveness across a variety of graph reasoning tasks and introducing a novel representational hierarchy. Learn more →goo.gle/3P7l72k
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Today on the blog, we demonstrate the feasibility & benefits of learning from human feedback for text-to-image generation by obtaining rich human feedback, then training a model to predict this feedback to enable improved image generation. Learn more →goo.gle/4c9i2Zz
ALT An illustration of our annotation user interface.
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Keeping You Connected: How AI/ML Proactively Monitors Mobile Networks aimllabs.io/keeping-you-conn…
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3 May 2024
A Look at the Evolving 6G Core Network and its Differences from 5G telecomgrid.com/5g/a-look-at…
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TimesFM is a forecasting model, pre-trained on a large time-series corpus of 100 billion real world time-points, that displays impressive zero-shot performance on a variety of public benchmarks from different domains and granularities. Learn more → goo.gle/480VRlm
ALT TimesFM architecture
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14 Jan 2024
Artificial intelligence helps unlock advances in #wireless communications #AI
techxplore.com/news/2024-01-…
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22 Nov 2023
New @5GAmericas briefing paper examines how devices are evolving to take advantage of #5G #wireless #networks.
Read more here: 5gamericas.org/evolving-devi…
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When applying #DifferentialPrivacy to multi-step #ML algorithms, there is a privacy loss that scales with each step, known as the cost of composition. Learn about a new paradigm that avoids this composition cost for significant improvements in utility. ↓goo.gle/44NMl3z
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A new generalization of automatic differentiation lets researchers compute upper and lower bounds on functions, instead of just derivatives, leading toward more principled learning rate selection during #ML model training. Learn more: goo.gle/417iphD
ALT Automatically-derived quadratic upper and lower bounds on a one-dimensional function f, centered at x-sub-0=0.5. The upper and lower bounds are valid over a user-specified trust region, and become tighter as the trust region shrinks.
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Announcing GPT-4, a large multimodal model, with our best-ever results on capabilities and alignment: openai.com/product/gpt-4
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Information on ChatGPT’s alignment, plans to improve it, giving users more control, and early thoughts on public input: openai.com/blog/how-should-a…
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Great #ML research requires great systems. Here we discuss some strategies we’re using to help serve and train sophisticated ML models while easing the complexity of implementation for end users. Read more at goo.gle/3JEbmr6
ALT Illustration of the Pathways system. Left: Distributed computation expressed as a Directed Acyclic Graph. Right: The resource manager allocates virtual slices of accelerator meshes for each compiled function (e.g., A, B, and C).
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Verbal disfluencies are common in day-to-day speech, and while people often don’t notice them, they can be very challenging for #NLP models. Check out a new #ML approach that removes disfluencies from speech transcripts for improved NLP performance. goo.gle/3I7p7Mc
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Introducing a novel approach for interpretable, robust, and reliable deep neural networks (DNNs) that employs controllable rule representations, which do not require retraining to adjust the rule strength at inference. Learn more below ↓ goo.gle/3IN8TGU
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Understanding how neural networks reach decisions can be challenging, but is also valuable for uses from image analysis to scientific discovery. A new approach, called StylEx, discovers and visualizes how disentangled attributes affect a classifier → goo.gle/357gn9r
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Preparing text for processing by an #ML model often involves tokenization, in which text is split into smaller units (e.g., words or word segments). Today we present a new approach that speeds up the process by up to 8x compared to standard methods. goo.gle/3dKHhVi
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Making reasonable predictions about the future is an important capability for #ML agents to be most useful. Learn about a new approach that is self-supervised and learns a predictive model that can be applied, without fine-tuning, to a variety of tasks ↓ goo.gle/3ksZD13
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Clustering algorithms partition datasets into meaningful groups and are a key building block of unsupervised #ML. Learn about a new clustering algorithm that enhances privacy while maintaining or improving performance against existing benchmarks ↓ goo.gle/3m2sxWO
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#ReinforcementLearning (RL) agents have shown promising results across various activities, but often cannot generalize to new tasks, even if they are similar. Learn about a new approach that incorporates RL’s sequential structure to enhance generalization→goo.gle/3ikBJns
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