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Bipartisan law would force Internet giants including Google and Facebook to reveal search algorithms

Daily Mail - Science & tech

Google, Facebook and other internet giants would disclose the algorithms they use to return search results under new legislation proposed by US law makers. The bipartisan Filter Bubble Transparency Act also would require the online companies to offer users an unfiltered search option that delivers results without any algorithmic tinkering. Senator John Thune, a Republican from North Dakota, filed the bill on Friday. The legislation was co-sponsored by Republican senators Jerry Moran of Kansas and Marsha blackburn of Tennessee, as well as Democrats Richard Blumenthal of Connecticut and Mark Warner of Virginia. Senator John Thune, a Republican from North Dakota, filed the bipartisan'Filter Bubble Transparency Act,' which would require internet companies to reveal algorithms used to determine online searches The online firm, owned by Alphabet, like other internet companies relies on algorithms - a highly-specific set of instructions to computers - that track users' behavior and location Thune says the legislation is needed because'people are increasingly impatient with the lack of transparency,' on the internet, reports the Wall Street Journal.


Welcome BERT: Google's latest search algorithm to better understand natural language - Search Engine Land

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Note: By submitting this form, you agree to Third Door Media's terms. Google is making the largest change to its search system since the company introduced RankBrain, almost five-years ago. The company said this will impact 1 in 10 queries in terms of changing the results that rank for those queries. BERT started rolling out this week and will be fully live shortly. It is rolling out for English language queries now and will expand to other languages in the future.


Robotic hand made by Elon Musk's OpenAI learns to solve Rubik's Cube

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Last year we were amazed by the level of dexterity achieved by OpenAI's Dactyl system which was able to learn how to manipulate a cube block to display any commanded side/face.If you missed that article, read about it here. OpenAI then set themselves a harder task of teaching the robotic hand to solve a Rubik's cube. Quite a daunting task made no easier by the fact that it would use one hand which most humans would find it hard to do. OpenAI harnessed the power of neural networks which are trained entirely in simulation. However, one of the main challenges faced was to make the simulations as realistic as possible because physical factors like friction, elasticity etc. are very hard to model.


Bayesian Optimization with Unknown Search Space

arXiv.org Machine Learning

Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Bayesian optimization. We devise a strategy to guarantee that in iterative expansions of the search space, our method can find a point whose function value within epsilon of the objective function maximum. Without the need to specify any parameters, our algorithm automatically triggers a minimal expansion required iteratively. We derive analytic expressions for when to trigger the expansion and by how much to expand. We also provide theoretical analysis to show that our method achieves epsilon-accuracy after a finite number of iterations. We demonstrate our method on both benchmark test functions and machine learning hyper-parameter tuning tasks and demonstrate that our method outperforms baselines.


A robot hand taught itself to solve a Rubik's Cube after creating its own training regime

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Over a year ago, OpenAI, the San Francisco–based for-profit AI research lab, announced that it had trained a robotic hand to manipulate a cube with remarkable dexterity. That might not sound earth-shattering. But in the AI world, it was impressive for two reasons. First, the hand had taught itself how to fidget with the cube using a reinforcement-learning algorithm, a technique modeled on the way animals learn. Second, all the training had been done in simulation, but it managed to successfully translate to the real world.


Effect of choice of probability distribution, randomness, and search methods for alignment modeling in sequence-to-sequence text-to-speech synthesis using hard alignment

arXiv.org Machine Learning

EFFECT OF CHOICE OF PROBABILITY DISTRIBUTION, RANDOMNESS, AND SEARCH METHODS FOR ALIGNMENT MODELING IN SEQUENCE-TO-SEQUENCE TEXT -TO-SPEECH SYNTHESIS USING HARD ALIGNMENT Y usuke Y asuda 1, 2, Xin W ang 1, Junichi Y amagishi 1, 2 1 National Institute of Informatics, Japan 2 SOKENDAI, Japan yasuda@nii.ac.jp, wangxin@nii.ac.jp, jyamagis@nii.ac.jp ABSTRACT Sequence-to-sequence text-to-speech (TTS) is dominated by soft-attention-based methods. Recently, hard-attention-based methods have been proposed to prevent fatal alignment errors, but their sampling method of discrete alignment is poorly investigated. This research investigates various combinations of sampling methods and probability distributions for alignment transition modeling in a hard-alignment-based sequence-to-sequence TTS method called SSNT -TTS. We clarify the common sampling methods of discrete variables including greedy search, beam search, and random sampling from a Bernoulli distribution in a more general way. Furthermore, we introduce the binary Concrete distribution to model discrete variables more properly. The results of a listening test shows that deterministic search is more preferable than stochastic search, and the binary Concrete distribution is robust with stochastic search for natural alignment transition.



Rubik's Cube owner loses EU trademark for iconic puzzle's shape

FOX News

Fox News Flash top headlines for Oct. 24 are here. Check out what's clicking on Foxnews.com The owner of the Rubik's Cube has lost an appeal to regain the European Union trademark rights to the classic puzzle's iconic shape in a new twist to the ongoing legal drama. Rubik's Brand Ltd. lost the protection rights to the puzzle's shape in 2017, after the EU's top court ruled that law prevents the firm from having "a monopoly on technical solutions or functional characteristics of a product," Bloomberg reported. The EU General Court in Luxembourg upheld that decision on Thursday.


Why a robot that can 'solve' Rubik's Cube one-handed has the AI community at war

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OpenAI, a non-profit co-founded by Elon Musk, recently unveiled its newest trick: A robot hand that can'solve' Rubik's Cube. Whether this is a feat of science or mere prestidigitation is a matter of some debate in the AI community right now. In case you missed it, OpenAI posted an article on its blog last week titled "Solving Rubik's Cube With a Robot Hand." Based on this title, you'd be forgiven if you thought the research discussed in said article was about solving Rubik's Cube with a robot hand. Don't get me wrong, OpenAI created a software and machine learning pipeline by which a robot hand can physically manipulate a Rubik's Cube from an'unsolved' state to a solved one.


This robot can now solve a Rubik's cube with one hand

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Once again, a robot can do something I cannot do. Researchers at the artificial intelligence lab OpenAI just revealed that its humanoid robotic hand can solve a Rubik's cube. The researchers utilized a pair of neural networks to make it happen. The team has been working on this project, named Dactyl, since the middle of 2017, and they felt showing their robotic hand could solve a Rubik's cube would show it had adequate dexterity. It can now solve the cube about 60 percent of the time.