machinelearning   50883

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explained.ai
Deep explanations of machine learning and related topics, often including visualizations.
machinelearning  reference  ml 
yesterday by tobym
PyTorch - an open-source machine learning library for Python - Wikipedia
PyTorch is an open-source machine learning library for Python, based on Torch,[1][2][3] used for applications such as natural language processing.[4] It is primarily developed by Facebook's artificial-intelligence research group,[5][6][7] and Uber's "Pyro" software for probabilistic programming is built on it.[8]
python  machinelearning  opensource  libraries 
yesterday by Chirael
Dark knowledge
Use "soft targets" (temperature-smoothed average predictions from ensemble models) to train smaller summary models - they carry much of the information from the teacher models.

Random dropout can achieve a similar effect more cheaply.

Ensembles-of-specialists can perform well, but must be combined with care.
computers  machinelearning  deeplearning  ai  google 
yesterday by pozorvlak
Alibaba already has a voice assistant way better than Google’s - MIT Technology Review
Alibaba is also developing digital assistants for other aspects of its business, including a food-ordering agent that can take your order in noisy restaurants and stores; a humanlike virtual avatar that can field questions about Alibaba products; and a price-haggling chatbot that is already used by 20% of sellers on Alibaba’s resale platform Xianyu.
machinelearning  AI  might_write  Leaders 
2 days ago by seatrout
The seductive diversion of ‘solving’ bias in artificial intelligence • Medium
Julia Powles and Helen Nissenbaum:
<p>What has been remarkably underappreciated is the key interdependence of the twin stories of A.I. inevitability and A.I. bias. Against the corporate projection of an otherwise sunny horizon of unstoppable A.I. integration, recognizing and acknowledging bias can be seen as a strategic concession — one that subdues the scale of the challenge. Bias, like job losses and safety hazards, becomes part of the grand bargain of innovation.

The reality that bias is primarily a social problem and cannot be fully solved technically becomes a strength, rather than a weakness, for the inevitability narrative. It flips the script. It absorbs and regularizes the classification practices and underlying systems of inequality perpetuated by automation, allowing relative increases in “fairness” to be claimed as victories — even if all that is being done is to slice, dice, and redistribute the makeup of those negatively affected by actuarial decision-making.

In short, the preoccupation with narrow computational puzzles distracts us from the far more important issue of the colossal asymmetry between societal cost and private gain in the rollout of automated systems. It also denies us the possibility of asking: Should we be building these systems at all?

The endgame is always to “fix” A.I. systems, never to use a different system or no system at all.
In accepting the existing narratives about A.I., vast zones of contest and imagination are relinquished. What is achieved is resignation — the normalization of massive data capture, a one-way transfer to technology companies, and the application of automated, predictive solutions to each and every societal problem.

Given this broader political and economic context, it should not surprise us that many prominent voices sounding the alarm on bias do so with blessing and support from the likes of Facebook, Microsoft, Alphabet, Amazon, and Apple. These convenient critics spotlight important questions, but they also suck attention from longer-term challenges. The endgame is always to “fix” A.I. systems, never to use a different system or no system at all.</p>
artificialintelligence  machinelearning  bias 
2 days ago by charlesarthur

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