specific insight, idea, or proprietary knowledge. It reflects how much that knowledge could be worth if applied effectively, even if it hasn’t yet generated revenue. Source: Microsoft, ‘Governing AI: A Blueprint Stanford computer scientist John McCarthy convenes the Dartmouth Conference on ‘Artificial Intelligence,’ a term he coined 1/62: Arthur Samuel, an IBM computer scientist, creates a self-learning Journal of Medicine Ambient artificial intelligence (AI) scribes, which use machine learning applied to conversations to facilitate scribe-like capabilities in real time, [have] great potential to

industry. 2. We plan to build TVM team in China, based in Shanghai, Beijing and Shenzhen. 1. Applied Scientist and SDE positions 2. Internship for students interested in ML system. 3. Research & Development

unifying features of Julia: functions are defined on different combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical accuracy encountered when computing with them, see David Goldberg's paper What Every Computer Scientist Should Know About Floating-Point Arithmetic. • For even more extensive documentation of the history not a numeric literal, when immediately followed by a parenthetical, is interpreted as a function applied to the values in parentheses (see Functions for more about functions). Thus, in both of these cases

unifying features of Julia: functions are defined on different combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical accuracy encountered when computing with them, see David Goldberg's paper What Every Computer Scientist Should Know About Floating-Point Arithmetic.CHAPTER 4. INTEGERS AND FLOATING-POINT NUMBERS 22 not a numeric literal, when immediately followed by a parenthetical, is interpreted as a function applied to the values in parentheses (see Functions for more about functions). Thus, in both of these cases

unifying features of Julia: functions are defined on different combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical accuracy encountered when computing with them, see David Goldberg's paper What Every Computer Scientist Should Know About Floating-Point Arithmetic. • For even more extensive documentation of the history not a numeric literal, when immediately followed by a parenthetical, is interpreted as a function applied to the values in parentheses (see Functions for more about functions). Thus, in both of these cases

unifying features of Julia: functions are defined on different combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical accuracy encountered when computing with them, see David Goldberg's paper What Every Computer Scientist Should Know About Floating-Point Arithmetic. • For even more extensive documentation of the history not a numeric literal, when immediately followed by a parenthetical, is interpreted as a function applied to the values in parentheses (see Functions for more about functions). Thus, in both of these cases

unifying features of Julia: functions are defined on different combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical accuracy encountered when computing with them, see David Goldberg's paper What Every Computer Scientist Should Know About Floating-Point Arithmetic. • For even more extensive documentation of the history not a numeric literal, when immediately followed by a parenthetical, is interpreted as a function applied to the values in parentheses (see Functions for more about functions). Thus, in both of these cases

unifying features of Julia: functions are defined on different combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical accuracy encountered when computing with them, see David Goldberg's paper What Every Computer Scientist Should Know About Floating-Point Arithmetic. • For even more extensive documentation of the history not a numeric literal, when immediately followed by a parenthetical, is interpreted as a function applied to the values in parentheses (see Functions for more about functions). Thus, in both of these cases

unifying features of Julia: functions are defined on different combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical accuracy encountered when computing with them, see David Goldberg's paper What Every Computer Scientist Should Know About Floating-Point Arithmetic. • For even more extensive documentation of the history not a numeric literal, when immediately followed by a parenthetical, is interpreted as a function applied to the values in parentheses (see Functions for more about functions). Thus, in both of these cases

unifying features of Julia: functions are defined on different combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical accuracy encountered when computing with them, see David Goldberg's paper What Every Computer Scientist Should Know About Floating-Point Arithmetic. • For even more extensive documentation of the history not a numeric literal, when immediately followed by a parenthetical, is interpreted as a function applied to the values in parentheses (see Functions for more about functions). Thus, in both of these cases