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Machine learning requires two things: one is smart algorithms; the other is data on which those algorithms can be trained.
Students learn how eliminating discrimination in machine learning requires more than simply reducing bias in the technical sense.
We argue that enabling human-in-the-loop machine learning requires establishing users' trust in computer-assisted analysis.
The problem is that this type of "machine learning" requires users' messages and other data to be routed through corporate servers so that they can be analysed and an appropriate response sent, as well as informing future interactions.
Machine learning requires a lot of data.
Machine learning requires lots of data to create, test and "train" the AI.
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Automatic emotion recognition systems based on supervised machine learning require reliable annotation of affective behaviours to build useful models.
In particular, promising near-term algorithms for quantum simulation, optimization, and machine learning require a hybrid quantum/classical programming environment.
Some data scientists I've talked to will go so far as to say that consumer startups that start to grow fast need a data scientist as part of the core engineering team as soon as possible, because most engineers working in the consumer space don't have the skills in statistics and/or machine learning required to make sense of the data.
This experiment could become one of Klick's first forays into machine learning, which requires a significant volume of data.
Performing large-scale data analysis and machine learning often requires addressing numerical linear algebra primitives, such as L2 regression, singular value decompositions, L1 regression, and canonical correlations.
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