A:Kerensky was an active member of the Grand Orient of Russia's Peoples, a traditional Freemasonic lodge which derived from the irregular Grand Orient of France. Kerensky was Secretary-General of the Grand Orient of Russia's Peoples and stood down following his ascent to the government in July 1917. He was succeeded by a Menshevik, Alexander Halpern. B:Although the meanings of most aspects of Freemasonry are kept secret, if an outsider were to see a building belonging to Kerensky's lodge, would they expect to see similar symbols and words on the public-facing exterior? Answer: engage
A:Kerensky was an active member of the Grand Orient of Russia's Peoples, which derived from the Grand Orient of France, and was considered to be an irregular lodge of the Freemasons. Kerensky was Secretary-General of the Grand Orient of Russia's Peoples and stood down following his ascent to the government in July 1917. He was succeeded by a Menshevik, Alexander Halpern. B:Does the passage imply that Freemasonic lodges tend to keep evolving, meaning that if one were to deviate from the mainstream, further spin-off lodges would continue that trend of deviation? Answer: engage
A:Kerensky was an active member of the irregular Freemasonic lodge, the Grand Orient of Russia's Peoples, which derived from the Grand Orient of France. Kerensky was Secretary-General of the Grand Orient of Russia's Peoples and stood down following his ascent to the government in July 1917. He was succeeded by a Menshevik, Alexander Halpern. B:If members of Kerensky's lodge were to meet and socialize with mainstream Freemasons, would they be wearing similar insignia and using similar phrases? Answer: spots
A:But these methods never won over the non-uniform internal-handcrafting Gaussian mixture model/Hidden Markov model (GMM-HMM) technology based on generative models of speech trained discriminatively. A number of key difficulties had been methodologically analyzed in the 1990s, including gradient diminishing and weak temporal correlation structure in the neural predictive models. All these difficulties were despite the availability of big training data and big computing power in these early days. Most speech recognition researchers who understood such barriers hence subsequently moved away from neural nets to pursue generative modeling approaches until the recent resurgence of deep learning starting around 2009–2010 that had overcome all these difficulties. Hinton et al. and Deng et al. reviewed part of this recent history about how their collaboration with each other and then with colleagues across four groups (University of Toronto, Microsoft, Google, and IBM) ignited a renaissance of applications of deep feedforward neural networks to speech recognition. B:If the big training data of 2010 was available in the 1990s, would speech recognition researchers have made great progress? Answer: spots
A:Kerensky was an active member of the irregular Freemasonic lodge, the Grand Orient of Russia's Peoples, which derived from the Grand Orient of France. Kerensky was Secretary-General of the Grand Orient of Russia's Peoples and stood down following his ascent to the government in July 1917. He was succeeded by a Menshevik, Alexander Halpern. B:Does the passage imply that Freemasonic lodges tend to keep evolving, meaning that if one were to deviate from the mainstream, further spin-off lodges would continue that trend of deviation? Answer: engage
A:Kerensky was an active member of the Grand Orient of Russia's Peoples, which derived from the Grand Orient of France, and was considered to be an irregular lodge of the Freemasons. Kerensky was Secretary-General of the Grand Orient of Russia's Peoples and stood down following his ascent to the government in July 1917. He was succeeded by a Menshevik, Alexander Halpern. B:Although the meanings of most aspects of Freemasonry are kept secret, if an outsider were to see a building belonging to Kerensky's lodge, would they expect to see similar symbols and words on the public-facing exterior? Answer: spots
A:Kerensky was an active member of the irregular Freemasonic lodge, the Grand Orient of Russia's Peoples, which derived from the Grand Orient of France. Kerensky was Secretary-General of the Grand Orient of Russia's Peoples and stood down following his ascent to the government in July 1917. He was succeeded by a Menshevik, Alexander Halpern. B:Although the meanings of most aspects of Freemasonry are kept secret, if an outsider were to see a building belonging to Kerensky's lodge, would they expect to see similar symbols and words on the public-facing exterior? Answer: spots
A:Kerensky was an active member of the Grand Orient of Russia's Peoples, a traditional Freemasonic lodge which derived from the irregular Grand Orient of France. Kerensky was Secretary-General of the Grand Orient of Russia's Peoples and stood down following his ascent to the government in July 1917. He was succeeded by a Menshevik, Alexander Halpern. B:If members of Kerensky's lodge were to meet and socialize with mainstream Freemasons, would they be wearing similar insignia and using similar phrases? Answer: engage
A:But these methods never won over the non-uniform internal-handcrafting Gaussian mixture model/Hidden Markov model (GMM-HMM) technology based on generative models of speech trained discriminatively. A number of key difficulties had been methodologically analyzed in the 1990s, including gradient diminishing and weak temporal correlation structure in the neural predictive models. All these difficulties were in addition to the lack of big computing power (though not big training data) in these early days. Most speech recognition researchers who understood such barriers hence subsequently moved away from neural nets to pursue generative modeling approaches until the recent resurgence of deep learning starting around 2009–2010 that had overcome all these difficulties. Hinton et al. and Deng et al. reviewed part of this recent history about how their collaboration with each other and then with colleagues across four groups (University of Toronto, Microsoft, Google, and IBM) ignited a renaissance of applications of deep feedforward neural networks to speech recognition. B:If the big training data of 2010 was available in the 1990s, would speech recognition researchers have made great progress? Answer:
spots