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【单选题】
In the 1950s, the pioneers of artificial intelligence (AI) predicted that, by the end of this century, computers would be conversing with us at work and robots would be performing our housework. But as useful as computers are, they' re nowhere close to achieving anything remotely resembling these early aspirations for humanlike behavior. Never mind something as complex as conversation: the most powerful computers struggle to reliably recognize the shape of an object, the most elementary of tasks for a tenmonth-old kid. A growing group of AI researchers think they know where the field went wrong. The problem, the scientists say, is that AI has been trying to separate the highest, most abstract levels of thought, like language and mathematics, and to duplicate them with logical, step-by-step programs. A new movement in AI, on the other hand, takes a closer look at the more roundabout way in which nature came up with intelligence. Many of these researchers study evolution and natural adaptation instead of formal logic and conventional computer programs. Rather than digital computers and transistors, some want to work with brain cells and proteins. The results of these early efforts are as promising as they are peculiar, and the new nature-based AI movement is slowly but surely moving to the forefront of the field. Imitating the brain' s neural (神经) network is a huge step in the right direction, says computer scientist and biophysicist Michael Conrad, but is still misses an important aspect of natural intelligence. 'People tend to treat the brain as if it were made up of color-coded transistors,' he explains, 'but it's not simply a clever network of switches. There are lots of important things going on inside the brain cells themselves.' Specifically, Conrad believes that many of the brain's capabilities stem from the pattern-recognition proficiency of the individual molecules that make up each brain cell. The best way to build an artificially intelligent device, he claims, would be to build it around the same sort of molecular skills. Right now, the notion that conventional computers and software are fundamentally incapable of matching the processes that take place in the brain remains controversial. But if it proves true, then the efforts of Conrad and his fellow A1 rebels could turn out to be the only game in town. The author says that the powerful computers of today______.
A.
are capable of reliably recognizing the shape of an object
B.
are close to exhibiting humanlike behavior
C.
are not very different in their performance from those of the 50's
D.
still cannot communicate with people in a human language
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【简答题】甲有限责任公司设立时收到乙公司作为资本投入的原材料一批,合同约定该材料的价值为20万,增值税进项税额为2.6万,材料验收入库,做会计分录
【单选题】甲股份有限公司收到乙企业作为资本投入的一批原材料,该原材料成本为200万元,公允价值300万元,适用的增值税税率为16%。甲公司在入账时,下列说法中正确的有( )。
A.
甲公司应按200万元来确定乙企业在注册资本中享有的份额
B.
甲公司应按300万元来确定乙企业在注册资本中享有的份额
C.
甲公司应按300万元来确定原材料的入账金额,按351万元来确定乙企业在注册资本中享有的份额
D.
甲公司应按300万元来确定原材料的入账金额,按200万元来确定乙企业在注册资本中享有的份额
【多选题】甲公司属于增值税小规模纳税人,2019年9月1日收到乙公司作为资本投入的原材料一批,该批原材料的合同约定价值是1 500万元,增值税的进项税额为195万元,假设
A.
应该计入原材料的金额是1 500万元
B.
应该计入原材料的金额是1 695万元
C.
甲公司实收资本的数额是1 500万元
D.
甲公司实收资本的数额是1 695万元
【判断题】功放电路中,甲类的效率可最高达到 78.5%
A.
正确
B.
错误
【单选题】某企业最近几批产品的优质品率分别为86%、93%、91%,为了对下一批产品的优质品率进行抽样检验,确定必要的抽样数目时,P应选()
A.
86%
B.
90%
C.
93%
D.
91%
【判断题】功放电路中,甲类的效率可最高达到78.5%
A.
正确
B.
错误
【单选题】甲公司收到乙公司作为资本投入的原材料一批,该批原材料投资合同约定的价值(不含进项税额)为400000元,增值税进项税额为68000元。乙公司已开具了增值税专用发票。假设合同约定的价值与公允价值相符,该进项税额允许抵扣,不考虑其他因素,甲公司应计入实收资本的金额为( )元。
A.
400000
B.
468000
C.
424000
D.
366000
【单选题】某企业最近几批产品的优质品率分别为85%、82%、91%,为了对下一批产品的优质品率进行抽样检验,确定必要的抽样数目时,p应选()
A.
82%
B.
85%
C.
50%
D.
91 %
【单选题】某企业最近几批产品的优质品率分别为85%、82%、91%,为了对下一批产品的优质品率进行抽样检验,确定必要的抽样数目时,p应选( )
A.
82%
B.
91%
C.
50%
D.
85%
【单选题】某企业最近几批产品的优质品率分别是88%、85%、91%,为了对下一批产品的优质品率进行抽样检验,确定必要的抽样单位数时,优质品率应选( )。
A.
85%
B.
88%
C.
91%
D.
(85%+88%+91%)/3
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