Ray Kurzweil | NAMM.org 1 00:00:00,000 ⇒ 00:00:29,980 [Speaker 2] I really appreciate you being here. Thank you very much. Well, it’s great to be here. It’s great to be at NAMM. I used to come here twice, well, here once a year on the Chicago show. Oh, is that right? Yeah, starting in 83, I think. When we unveiled the Kurzweil 250, we had a prototype yet, it wasn’t in production, and we had private showings, and we got a very
2 00:00:29,980 ⇒ 00:00:59,960 [Speaker 1] enthusiastic response, so that was kind of the beginning. Oh, that’s neat. Tell me just a little brief history, if you would, on how music became so important to you. Did you have a lot growing up? Well, my father was a famous musician. He was conducting the Bell Symphony, which was the symphony orchestra, the Bell Telephone System. They were on TV a lot. He was head of the Pittsburgh Opera, Mobile Opera, Queens Concert Orchestra,
3 00:00:59,960 ⇒ 00:01:29,940 [Speaker 2] founded the music department at the Queensport College, and composer. So he taught me piano when I was six. Actually, in the 60s, he became very excited about the Moog synthesizer and switched on Bach. And I had an interest in computers at that time. I’d been building computers. And he said, you know, someday you’re going to combine computers and music, and do something with computer music, which really didn’t exist back then, because the first introduction
4 00:01:29,940 ⇒ 00:01:59,920 [Speaker 2] to combination of combination of technology and music was analog synthesis, which did take him, did pique his interest. He died in 1970, but I’ve always kept that sort of challenge in mind. My primary interest is pattern recognition as part of artificial intelligence, trying to emulate the pattern recognition capability of human beings. And I actually did a
5 00:01:59,920 ⇒ 00:02:17,910 [Speaker 2] project in the project in the 70s. I created the first Omni font, any type font character recognition. And then happened to sit next to a blind gentleman on a plane who said, blindness is not really a handicap. He can do anything. He’s traveling around the world, but he can’t really read ordinary print. And that was a handicap.
6 00:02:17,910 ⇒ 00:02:24,410 [Speaker 2] And so I then devoted that character recognition technology to the blind reading problem because
7 00:02:24,410 ⇒ 00:02:27,860 [Speaker 2] it was sort of a solution in search of a problem.
8 00:02:27,860 ⇒ 00:02:29,970 [Speaker 2] And we had to create two other technologies.
9 00:02:29,970 ⇒ 00:02:33,910 [Speaker 2] We created the first flatbed scanner and the first speech synthesis.
10 00:02:33,910 ⇒ 00:02:41,020 [Speaker 2] And we combined those three technologies, which today are ubiquitous, character recognition,
11 00:02:41,020 ⇒ 00:02:45,520 [Speaker 2] flatbed scanning, and full text-to-speech synthesis and created the first print-to-speech
12 00:02:45,520 ⇒ 00:02:48,240 [Speaker 2] reading machine for the blind.
13 00:02:48,240 ⇒ 00:02:51,900 [Speaker 2] And we introduced it.
14 00:02:51,900 ⇒ 00:02:53,760 [Speaker 2] It was on all three nightly networks.
15 00:02:53,760 ⇒ 00:02:59,360 [Speaker 2] Walter Crockett used it for his signature sign-off, and that’s the way it was, January 13, 1976.
16 00:02:59,360 ⇒ 00:03:06,120 [Speaker 2] A few days later, I was on the Today Show, and Stevie Wonder happened to catch that show.
17 00:03:06,120 ⇒ 00:03:10,420 [Speaker 2] And he called us up.
18 00:03:10,420 ⇒ 00:03:13,120 [Speaker 2] Our receptionist didn’t really believe it was him, but put him through anyway.
19 00:03:13,120 ⇒ 00:03:15,840 [Speaker 2] Anyway, it said, “Well, this is amazing.
20 00:03:15,840 ⇒ 00:03:20,060 [Speaker 2] I have to stop by, and I want to buy one.”
21 00:03:20,060 ⇒ 00:03:23,980 [Speaker 2] We actually didn’t have one, but we very quickly finished one up.
22 00:03:23,980 ⇒ 00:03:25,360 [Speaker 2] He came by.
23 00:03:25,360 ⇒ 00:03:26,840 [Speaker 2] We spent a few hours showing him how to use it.
24 00:03:26,840 ⇒ 00:03:31,840 [Speaker 2] He went off in a taxi with his reading machine, Kurzweil reading machine.
25 00:03:31,840 ⇒ 00:03:34,420 [Speaker 2] It was our first customer.
26 00:03:34,420 ⇒ 00:03:39,860 [Speaker 2] But that was 1976, and that started a relationship which has continued to this day.
27 00:03:39,860 ⇒ 00:03:42,660 [Speaker 2] So that’s 30 years now.
28 00:03:42,660 ⇒ 00:03:46,280 [Speaker 2] And so over the years we had a lot of discussions.
29 00:03:46,280 ⇒ 00:03:54,660 [Speaker 2] He’s actually pretty savvy about technology, both because it’s a great equalizer for disabilities,
30 00:03:54,660 ⇒ 00:03:58,680 [Speaker 2] and because technology plays such an important role in music.
31 00:03:58,680 ⇒ 00:04:05,500 [Speaker 2] And in 1982 he was showing me around a new studio he had called Wonderland here in Los Angeles.
32 00:04:05,500 ⇒ 00:04:10,300 [Speaker 2] And it was lamenting the state of affairs that there was really these two disconnected worlds
33 00:04:10,300 ⇒ 00:04:11,440 [Speaker 2] of musical instruments.
34 00:04:11,440 ⇒ 00:04:18,260 [Speaker 2] There was the electronic world where you could do, you had all these tremendous control capabilities.
35 00:04:18,260 ⇒ 00:04:23,240 [Speaker 2] You could play one part and the computer would remember it, and then you could play it back
36 00:04:23,240 ⇒ 00:04:29,620 [Speaker 2] from the computer’s memory and play another line over it, and you could build up multi-line orchestrations.
37 00:04:29,620 ⇒ 00:04:32,040 [Speaker 2] You could modify the sounds.
38 00:04:32,040 ⇒ 00:04:39,080 [Speaker 2] But the sounds you had to work with in that electronic world were very thin, synthetic sounding sounds.
39 00:04:39,080 ⇒ 00:04:43,520 [Speaker 2] And then there was these sounds of 19th century acoustic instruments like the piano, guitar, violin.
40 00:04:43,520 ⇒ 00:04:51,260 [Speaker 2] And these were still the sounds of choice of musicians because they had a lot of deep, complex resonance.
41 00:04:51,260 ⇒ 00:04:54,380 [Speaker 2] But they’re very hard to play.
42 00:04:54,380 ⇒ 00:04:58,360 [Speaker 2] I mean most musicians can’t play most instruments.
43 00:04:58,360 ⇒ 00:05:02,390 [Speaker 2] And even if you’re a virtuoso and can play them all, you can’t play them simultaneously.
44 00:05:02,390 ⇒ 00:05:03,980 [Speaker 2] Most of you can only play one note at a time.
45 00:05:03,980 ⇒ 00:05:08,440 [Speaker 2] So you can’t modify the sounds except maybe a few modification techniques.
46 00:05:08,440 ⇒ 00:05:12,240 [Speaker 2] You can do vibrato and a violin, but it’s very limited.
47 00:05:12,240 ⇒ 00:05:17,640 [Speaker 2] So wouldn’t it be great if we could take this very rich array of control techniques that you
48 00:05:17,640 ⇒ 00:05:22,700 [Speaker 2] have in the electronic world and apply it to these very rich, complex sounds of choice of the
49 00:05:22,700 ⇒ 00:05:24,320 [Speaker 2] acoustic world.
50 00:05:24,320 ⇒ 00:05:27,600 [Speaker 2] And then maybe you could actually create new sounds that have the complexity of acoustic
51 00:05:27,600 ⇒ 00:05:30,960 [Speaker 2] sounds but aren’t sounds that any acoustic instrument could make.
52 00:05:30,960 ⇒ 00:05:36,440 [Speaker 2] You could open up a whole new world of synthetic sounds that weren’t so simple and synthetic and
53 00:05:36,440 ⇒ 00:05:41,660 [Speaker 2] thin as the electronic world was used to at that time.
54 00:05:41,660 ⇒ 00:05:45,660 [Speaker 2] And I felt actually using advanced signal processing and some pattern recognition insights that
55 00:05:45,660 ⇒ 00:05:48,060 [Speaker 2] we could do that.
56 00:05:48,060 ⇒ 00:05:52,780 [Speaker 2] And so we started Kurzweil Music then in 1982, July 1st actually.
57 00:05:52,780 ⇒ 00:05:56,400 [Speaker 2] C.V. Wonder was our advisor.
58 00:05:56,400 ⇒ 00:06:01,640 [Speaker 2] We set to create the Kurzweil 250, the first instrument that could really recreate the acoustic
59 00:06:01,640 ⇒ 00:06:03,300 [Speaker 2] piano.
60 00:06:03,300 ⇒ 00:06:05,440 [Speaker 2] And there are a lot of challenges.
61 00:06:05,440 ⇒ 00:06:08,660 [Speaker 2] You might think that, well, you just sample the piano and it’ll sound like a piano.
62 00:06:08,660 ⇒ 00:06:13,660 [Speaker 2] But samplers, particularly at that time, for example, would loop the last waveform and then
63 00:06:13,660 ⇒ 00:06:16,220 [Speaker 2] have a decaying envelope.
64 00:06:16,220 ⇒ 00:06:21,220 [Speaker 2] That doesn’t work for the piano because the overtones in a piano are not perfect multiples of the fundamental.
65 00:06:21,220 ⇒ 00:06:23,220 [Speaker 2] They’re a little off.
66 00:06:23,220 ⇒ 00:06:24,220 [Speaker 2] They’re called anharmonic.
67 00:06:24,220 ⇒ 00:06:26,220 [Speaker 2] That gives the piano its sort of rich character.
68 00:06:26,220 ⇒ 00:06:29,720 [Speaker 2] Well, if you loop one waveform, then all the overtones become perfect multiples.
69 00:06:29,720 ⇒ 00:06:34,100 [Speaker 2] It sounds like an organ tone and it loses its piano character.
70 00:06:34,100 ⇒ 00:06:36,780 [Speaker 2] So that’s an insight from pattern recognition.
71 00:06:36,780 ⇒ 00:06:41,460 [Speaker 2] But that’s very challenging for sampling, particularly then when memory was expensive because you can’t
72 00:06:41,460 ⇒ 00:06:47,640 [Speaker 2] really afford to record the entire 20-second decay of a piano note.
73 00:06:47,640 ⇒ 00:06:50,640 [Speaker 2] If you hit a middle C harder, it’s not just louder.
74 00:06:50,640 ⇒ 00:06:57,840 [Speaker 2] It has a whole different time-bearing pitch contour.
75 00:06:57,840 ⇒ 00:07:03,820 [Speaker 2] The different volume levels have a completely different time-bearing timbre.
76 00:07:03,820 ⇒ 00:07:06,880 [Speaker 2] And you can’t really capture all of that with sampling.
77 00:07:06,880 ⇒ 00:07:11,640 [Speaker 2] And if you try to bend the pitch too much, it changes the character of the tones in unrealistic
78 00:07:11,640 ⇒ 00:07:12,640 [Speaker 2] ways.
79 00:07:12,640 ⇒ 00:07:15,980 [Speaker 2] The tones won’t interact with each other when you put the pedal down.
80 00:07:15,980 ⇒ 00:07:21,400 [Speaker 2] So a lot of complexities of real-life piano that you really can’t capture with sampling unless
81 00:07:21,400 ⇒ 00:07:25,440 [Speaker 2] you add some other elements of signal processing and pattern recognition.
82 00:07:25,440 ⇒ 00:07:26,440 [Speaker 2] So that’s what we sought to do.
83 00:07:26,440 ⇒ 00:07:37,440 [Speaker 2] And we came actually to my first NAMM show, which was here in Anaheim in ‘83, and showed
84 00:07:37,440 ⇒ 00:07:39,440 [Speaker 2] our prototype of the Kurzweil 250.
85 00:07:39,440 ⇒ 00:07:43,780 [Speaker 2] And people were pretty much blown away because it really did sound like a piano.
86 00:07:43,780 ⇒ 00:07:45,440 [Speaker 2] We started shipping it in ‘84.
87 00:07:45,440 ⇒ 00:07:52,780 [Speaker 2] And it did get recognized as the first electronic instrument that could recreate the piano, which
88 00:07:52,780 ⇒ 00:07:54,280 [Speaker 2] really is the most challenging.
89 00:07:54,280 ⇒ 00:07:57,440 [Speaker 2] But it also could do other orchestral instruments.
90 00:07:57,440 ⇒ 00:08:02,500 [Speaker 2] And it has maintained its sort of edge in terms of sound quality and realism, particularly
91 00:08:02,500 ⇒ 00:08:04,540 [Speaker 2] of acoustic instruments.
92 00:08:04,540 ⇒ 00:08:07,880 [Speaker 2] But sound quality in general.
93 00:08:07,880 ⇒ 00:08:09,440 [Speaker 2] So that’s how we got started.
94 00:08:09,440 ⇒ 00:08:16,440 [Speaker 1] You know, I interviewed a former president of Baldwin, and I asked him, you know, was there
95 00:08:16,440 ⇒ 00:08:21,440 [Speaker 1] any point during his career that they worried about the synthesizer replacing the piano?
96 00:08:21,440 ⇒ 00:08:24,440 [Speaker 1] And he says, “Not until the Kurzweil came out.
97 00:08:24,440 ⇒ 00:08:25,440 [Speaker 1] Then we started worrying.”
98 00:08:25,440 ⇒ 00:08:26,440 [Speaker 1] Yeah.
99 00:08:26,440 ⇒ 00:08:33,440 [Speaker 2] Well, I think, you know, digital pianos for the home market have significantly cut into the,
100 00:08:33,440 ⇒ 00:08:35,440 [Speaker 2] particularly the low-end market.
101 00:08:35,440 ⇒ 00:08:39,440 [Speaker 2] You know, parents that want to buy a piano for their eight-year-old child.
102 00:08:39,440 ⇒ 00:08:42,440 [Speaker 2] Because there are a lot of advantages to electronic piano.
103 00:08:42,440 ⇒ 00:08:49,440 [Speaker 2] For the same price range, you can get a better quality sound in a digital piano than an acoustic.
104 00:08:49,440 ⇒ 00:08:57,440 [Speaker 2] You know, at the high end, there’s still a market and still an advantage for acoustic pianos.
105 00:08:57,440 ⇒ 00:09:04,440 [Speaker 2] But if you’re talking about just a routine piano for Sally to take piano lessons, you get a
106 00:09:04,440 ⇒ 00:09:05,440 [Speaker 2] lot of value.
107 00:09:05,440 ⇒ 00:09:07,440 [Speaker 2] And then there’s a lot of other capabilities, you know.
108 00:09:07,440 ⇒ 00:09:12,440 [Speaker 2] When Sally learns how to play a piece, she can also play it on the violin and the human
109 00:09:12,440 ⇒ 00:09:14,440 [Speaker 2] voice and all these other instruments.
110 00:09:14,440 ⇒ 00:09:24,440 [Speaker 2] And then electronic instruments have autoplay and can help teach you how to play the piano,
111 00:09:24,440 ⇒ 00:09:27,440 [Speaker 2] can record what you’re playing with sequencers.
112 00:09:27,440 ⇒ 00:09:30,440 [Speaker 2] So you have none of those capabilities in an acoustic piano.
113 00:09:30,440 ⇒ 00:09:32,440 [Speaker 2] You don’t have to tune them.
114 00:09:32,440 ⇒ 00:09:38,440 [Speaker 2] You can use headphones so people can practice without disturbing other people.
115 00:09:38,440 ⇒ 00:09:39,440 [Speaker 2] So a lot of advantages.
116 00:09:39,440 ⇒ 00:09:45,440 [Speaker 2] And it has really cut into the, I’d say, the upright piano market.
117 00:09:45,440 ⇒ 00:09:46,440 [Speaker 2] Interesting.
118 00:09:46,440 ⇒ 00:09:54,440 [Speaker 1] And one of the things that I think that you guys set out to do was not just a piano, but
119 00:09:54,440 ⇒ 00:09:58,440 [Speaker 1] what other musical instruments can we recreate?
120 00:09:58,440 ⇒ 00:10:04,440 [Speaker 2] Well, the piano is the most challenging, but it really can recreate any orchestral instrument.
121 00:10:04,440 ⇒ 00:10:10,440 [Speaker 2] And also can then break down these instruments into their components and create synthetic sounds.
122 00:10:10,440 ⇒ 00:10:16,440 [Speaker 2] That is, you know, a new sound that no acoustic instrument could create but has the complexity
123 00:10:16,440 ⇒ 00:10:18,440 [Speaker 2] and richness of an acoustic sound.
124 00:10:18,440 ⇒ 00:10:23,440 [Speaker 2] Maybe because you started with an acoustic sound and modified it to be unrecognizable, but
125 00:10:23,440 ⇒ 00:10:25,440 [Speaker 2] it nonetheless keeps its complex character.
126 00:10:25,440 ⇒ 00:10:36,440 [Speaker 2] So, you know, if you just start from the ground up doing, say, analog synthesis, that was a very groundbreaking
127 00:10:36,440 ⇒ 00:10:39,440 [Speaker 2] development when it occurred and it was a new class of sounds.
128 00:10:39,440 ⇒ 00:10:45,440 [Speaker 2] But there’s a limited complexity you can create by just building up oscillators.
129 00:10:45,440 ⇒ 00:10:50,440 [Speaker 2] By starting with the richness and complexity of acoustic sounds and then modifying it using
130 00:10:50,440 ⇒ 00:10:56,440 [Speaker 2] a whole panoply of signal processing methods, you can, you know, keep the complexity and the
131 00:10:56,440 ⇒ 00:11:00,440 [Speaker 2] musical depth but create a whole new class of sounds.
132 00:11:00,440 ⇒ 00:11:03,440 [Speaker 2] And then you can modify it with all kinds of other synthesis techniques.
133 00:11:03,440 ⇒ 00:11:12,440 [Speaker 2] And so we have a new chip now that does a significant amount of digital signal processing on each channel.
134 00:11:12,440 ⇒ 00:11:17,440 [Speaker 2] So you could start with the sampled sound but then modify it by putting it basically through
135 00:11:17,440 ⇒ 00:11:27,440 [Speaker 2] a whole complex set of synthesizer sound modification capabilities per channel and then apply more
136 00:11:27,440 ⇒ 00:11:30,440 [Speaker 2] signal processing to the mix sound.
137 00:11:30,440 ⇒ 00:11:33,440 [Speaker 2] So there are a lot of capabilities.
138 00:11:33,440 ⇒ 00:11:37,440 [Speaker 2] And one of the things, I mean, another whole interest I have is in tracking technology trends.
139 00:11:37,440 ⇒ 00:11:43,440 [Speaker 2] And I did that because of my interest in being an inventor and because I realized timing was critical
140 00:11:43,440 ⇒ 00:11:46,440 [Speaker 2] and most inventions fail because the timing is wrong.
141 00:11:46,440 ⇒ 00:11:50,440 [Speaker 2] And so I developed these models of how technology evolves.
142 00:11:50,440 ⇒ 00:11:53,440 [Speaker 2] And it evolves actually in very predictable ways.
143 00:11:53,440 ⇒ 00:12:06,440 [Speaker 2] I have a theory called the law of accelerating returns that indicates, that says that information technology in many different areas is basically doubling its power every year in terms of price performance.
144 00:12:06,440 ⇒ 00:12:07,440 [Speaker 2] And capacity.
145 00:12:07,440 ⇒ 00:12:11,440 [Speaker 2] Doubling every year is very phenomenal exponential growth.
146 00:12:11,440 ⇒ 00:12:13,440 [Speaker 2] That’s a factor of a thousand in ten years.
147 00:12:13,440 ⇒ 00:12:15,440 [Speaker 2] A million in 20 years.
148 00:12:15,440 ⇒ 00:12:18,440 [Speaker 2] A billion in 30 years.
149 00:12:18,440 ⇒ 00:12:23,440 [Speaker 2] So it’s been over 20 years since we introduced the Curso 250.
150 00:12:23,440 ⇒ 00:12:31,440 [Speaker 2] So in those 20 years, information technology, computer technology, digital signal processing, it’s all gotten a million times more powerful.
151 00:12:31,440 ⇒ 00:12:45,440 [Speaker 2] So, you know, we can now do in a low end instrument, you know, thousands of times more transformations in capability than was feasible in an expensive instrument 20 years ago.
152 00:12:45,440 ⇒ 00:12:46,440 [Speaker 2] So, you know, that’s going to continue.
153 00:12:46,440 ⇒ 00:12:52,440 [Speaker 2] You know, because I have this whole new phenomenon of software synthesis where with a PC you can do some pretty impressive things.
154 00:12:52,440 ⇒ 00:12:59,440 [Speaker 1] How long did you stay focused in the company as far as developing products?
155 00:12:59,440 ⇒ 00:13:03,440 [Speaker 2] Well, I started the company in ‘82.
156 00:13:03,440 ⇒ 00:13:05,440 [Speaker 2] We introduced Curso 250 in ‘84.
157 00:13:05,440 ⇒ 00:13:10,440 [Speaker 2] We had our first chip based products, Curso 1000, a few years after that.
158 00:13:10,440 ⇒ 00:13:19,440 [Speaker 2] We sold the company to Yong Chang, a Korean piano manufacturer in 1990.
159 00:13:19,440 ⇒ 00:13:24,440 [Speaker 2] And I remained actively involved through ‘95.
160 00:13:24,440 ⇒ 00:13:34,440 [Speaker 2] After that I was not directly involved, although I stayed in close touch with all the engineers through the sort of ups and downs.
161 00:13:34,440 ⇒ 00:13:44,440 [Speaker 2] A number of key people, including myself, left around ‘95, ‘96, ‘97 in Yong Chang got into some financial difficulties in recent years.
162 00:13:44,440 ⇒ 00:13:49,440 [Speaker 2] And we were actually concerned how to revitalize the company.
163 00:13:49,440 ⇒ 00:14:00,440 [Speaker 2] I think that’s worked out actually quite well because Hyundai has bought the company and they have very substantial resources and they know how to manufacture high quality products.
164 00:14:00,440 ⇒ 00:14:02,440 [Speaker 2] So they’re revitalizing the company.
165 00:14:02,440 ⇒ 00:14:09,440 [Speaker 2] I think it’s going to be a strong combination with their resources and the Kurzweil brand and the Kurzweil core technology.
166 00:14:09,440 ⇒ 00:14:21,440 [Speaker 2] But the technology stayed intact and there was one key project, the Mara chip, which is some products being introduced here at NAMM are based on that, which remained.
167 00:14:21,440 ⇒ 00:14:26,440 [Speaker 2] And it’s really a very cutting edge chip.
168 00:14:26,440 ⇒ 00:14:29,440 [Speaker 2] So the core technology remains very strong.
169 00:14:29,440 ⇒ 00:14:33,440 [Speaker 2] There’s very strong sound where the key engineers have remained throughout.
170 00:14:33,440 ⇒ 00:14:37,440 [Speaker 2] Hyundai is now building up the company again.
171 00:14:37,440 ⇒ 00:14:40,440 [Speaker 2] Our research and development has quadrupled in the last year.
172 00:14:40,440 ⇒ 00:14:45,440 [Speaker 1] So back up a little second and ask you an off the wall question.
173 00:14:45,440 ⇒ 00:14:58,440 [Speaker 2] Oh, and I’m actually back now advising Kurzweil Music again, helping them with technology strategy and also being a spokesperson as the founder.
174 00:14:58,440 ⇒ 00:15:03,440 [Speaker 2] But yeah, I’m involved again pretty closely with the new team.
175 00:15:03,440 ⇒ 00:15:04,440 [Speaker 1] Excellent.
176 00:15:04,440 ⇒ 00:15:07,440 [Speaker 1] So is there going to be new products?
177 00:15:07,440 ⇒ 00:15:25,440 [Speaker 2] Yeah, there’s new products here, MP3, which is very impressive, 128 voices, highest, 32-bit voices, 128 of them.
178 00:15:25,440 ⇒ 00:15:31,440 [Speaker 2] Each voice has significant amount of digital signal processing capability per voice.
179 00:15:31,440 ⇒ 00:15:36,440 [Speaker 2] And then there’s a very sophisticated effects processor for the mixed output.
180 00:15:36,440 ⇒ 00:15:42,440 [Speaker 2] And lots of other features, advanced sequencers and sound modification.
181 00:15:42,440 ⇒ 00:15:52,440 [Speaker 2] So it’s a pretty high-end instrument, but it’s going to be, I mean, list price of $2,500, so street price will be less than that.
182 00:15:52,440 ⇒ 00:15:54,440 [Speaker 2] It’s actually quite impressive.
183 00:15:54,440 ⇒ 00:16:01,440 [Speaker 2] And then there’s a SP2, Stage Piano 2, which is kind of a lower-end version of that, but still very impressive.
184 00:16:01,440 ⇒ 00:16:04,440 [Speaker 2] Sixty-four voices, which is still a lot.
185 00:16:04,440 ⇒ 00:16:08,440 [Speaker 2] And that’ll be something like $1,500 at this price.
186 00:16:08,440 ⇒ 00:16:11,440 [Speaker 2] So those are pretty impressive products and they’re here.
187 00:16:11,440 ⇒ 00:16:15,440 [Speaker 2] In fact, Stevie Wonder was just playing them a couple hours ago.
188 00:16:15,440 ⇒ 00:16:16,440 [Speaker 2] How great.
189 00:16:16,440 ⇒ 00:16:25,440 [Speaker 1] I was going to ask you, what’s your, who’s your thought of the father of electronic music?
190 00:16:25,440 ⇒ 00:16:32,440 [Speaker 1] There’s a lot of names that float around in my head, and I wonder who you would consider that to be.
191 00:16:32,440 ⇒ 00:16:36,440 [Speaker 2] Well, I mean, Bob Moog really put it on the map.
192 00:16:36,440 ⇒ 00:16:39,440 [Speaker 2] He was inspired by theremin.
193 00:16:39,440 ⇒ 00:16:46,440 [Speaker 2] In fact, he started building theremins at a young age, but then took it in a whole new direction
194 00:16:46,440 ⇒ 00:16:55,440 [Speaker 2] and established the basis of synthesis using the technology of that time, which was analog synthesis and oscillators.
195 00:16:55,440 ⇒ 00:16:59,440 [Speaker 2] He created a whole new class of instrument and a whole new class of sound.
196 00:16:59,440 ⇒ 00:17:02,440 [Speaker 2] He created a lot of excitement.
197 00:17:02,440 ⇒ 00:17:11,440 [Speaker 2] I mean, switched on Bach, that album by Walter Carlos, I think he was Walter at that time, created a big buzz.
198 00:17:11,440 ⇒ 00:17:18,440 [Speaker 2] So my father, who was a classical musician, opera conductor and timpani conductor and concert pianist,
199 00:17:18,440 ⇒ 00:17:22,440 [Speaker 2] got very excited about that.
200 00:17:22,440 ⇒ 00:17:26,440 [Speaker 2] And it attracted tremendous interest from the classical world to the pop world.
201 00:17:26,440 ⇒ 00:17:31,440 [Speaker 2] And really put, I mean, created the synthesizer.
202 00:17:31,440 ⇒ 00:17:33,440 [Speaker 2] So there were roots to it.
203 00:17:33,440 ⇒ 00:17:39,440 [Speaker 2] He didn’t create it out of nothing, but that really was the beginning of the synthesizer.
204 00:17:39,440 ⇒ 00:17:46,440 [Speaker 2] And then there were various efforts for digital synthesizers and samplers.
205 00:17:46,440 ⇒ 00:17:48,440 [Speaker 2] Emu played an important role.
206 00:17:48,440 ⇒ 00:17:58,440 [Speaker 2] And our goal was actually to go beyond just sampling to bring some signal processing sophistication
207 00:17:58,440 ⇒ 00:18:05,440 [Speaker 2] to really capture some of these complex effects of acoustic instruments that you can’t just get by sampling.
208 00:18:05,440 ⇒ 00:18:08,440 [Speaker 2] Because samplers were pretty unsuccessful in capturing the piano.
209 00:18:08,440 ⇒ 00:18:14,440 [Speaker 1] We’re definitely going to have to do a part two one of these days, because I’m running out of time.
210 00:18:14,440 ⇒ 00:18:23,440 [Speaker 1] But I do want to ask you, do you have the tape of your 1965 television appearance?
211 00:18:23,440 ⇒ 00:18:25,440 [Speaker 1] Is that I got a secret?
212 00:18:25,440 ⇒ 00:18:26,440 [Speaker 2] Yeah.
213 00:18:26,440 ⇒ 00:18:33,440 [Speaker 2] In fact, I was shown recently, I did a three hour interview on a program called In Depth on Book TV, C-SPAN 2.
214 00:18:33,440 ⇒ 00:18:37,440 [Speaker 2] I started out the program by displaying that.
215 00:18:37,440 ⇒ 00:18:39,440 [Speaker 2] We can send you a copy.
216 00:18:39,440 ⇒ 00:18:41,440 [Speaker 1] I’d love to see it, yeah.
217 00:18:41,440 ⇒ 00:18:44,440 [Speaker 2] But yeah, actually, that was actually a music project I did in high school,
218 00:18:44,440 ⇒ 00:18:50,440 [Speaker 2] where I used pattern recognition to find patterns in melodies.
219 00:18:50,440 ⇒ 00:18:54,440 [Speaker 2] So I’d feed in Bach melodies or Chopin or Beethoven,
220 00:18:54,440 ⇒ 00:18:58,440 [Speaker 2] and it would actually model the types of patterns that those composers used,
221 00:18:58,440 ⇒ 00:19:01,440 [Speaker 2] and then compose original music using those patterns.
222 00:19:01,440 ⇒ 00:19:07,440 [Speaker 2] And it would sound like a second-rate student of Mozart or Chopin, as the case may be.
223 00:19:07,440 ⇒ 00:19:13,440 [Speaker 2] And I won some science contests, got to meet President Johnson.
224 00:19:13,440 ⇒ 00:19:17,440 [Speaker 2] I was invited on this network show.
225 00:19:17,440 ⇒ 00:19:21,440 [Speaker 2] There weren’t very many TV shows back then, so I’ve got a secret.
226 00:19:21,440 ⇒ 00:19:23,440 [Speaker 2] And my secret, I came on, I played a piece of music,
227 00:19:23,440 ⇒ 00:19:26,440 [Speaker 2] and my secret was that I built a computer that composed the music.
228 00:19:26,440 ⇒ 00:19:32,440 [Speaker 1] Did most Americans even know what a computer was then, you think?
229 00:19:32,440 ⇒ 00:19:36,440 [Speaker 2] Yeah, there had been publicity about these giant brains,
230 00:19:36,440 ⇒ 00:19:38,440 [Speaker 2] and there was already speculation about, you know,
231 00:19:38,440 ⇒ 00:19:41,440 [Speaker 2] are they thinking and what will they be able to do?
232 00:19:41,440 ⇒ 00:19:44,440 [Speaker 2] And some of these early computers were able to do things
233 00:19:44,440 ⇒ 00:19:48,440 [Speaker 2] that professional mathematicians had not been able to do.
234 00:19:48,440 ⇒ 00:19:56,440 [Speaker 2] Interestingly, ironically, the history of artificial intelligence is the opposite of human skill.
235 00:19:56,440 ⇒ 00:20:00,440 [Speaker 2] Computers very quickly learned how to do things that professional humans do,
236 00:20:00,440 ⇒ 00:20:04,440 [Speaker 2] like solving mathematical theorems, diagnosing disease.
237 00:20:04,440 ⇒ 00:20:09,440 [Speaker 2] But today they still struggle, say, telling the difference between a dog and a cat,
238 00:20:09,440 ⇒ 00:20:11,440 [Speaker 2] and things that a five-year-old child can do.
239 00:20:11,440 ⇒ 00:20:15,440 [Speaker 2] They’re actually learning how to tell a different machine, a dog and a cat.
240 00:20:15,440 ⇒ 00:20:16,440 [Speaker 2] But it’s been backwards.
241 00:20:16,440 ⇒ 00:20:18,440 [Speaker 2] They first learned to do what adults can do,
242 00:20:18,440 ⇒ 00:20:21,440 [Speaker 2] and now they’re backing up and learning what children can do.
243 00:20:21,440 ⇒ 00:20:27,440 [Speaker 2] But there had been a lot of publicity even in the ’50s about these giant brains
244 00:20:27,440 ⇒ 00:20:30,440 [Speaker 2] and what they will eventually be able to do.
245 00:20:30,440 ⇒ 00:20:33,440 [Speaker 2] And there was already perception of this accelerating returns.
246 00:20:33,440 ⇒ 00:20:36,440 [Speaker 2] Computers were getting twice as powerful every year.
247 00:20:36,440 ⇒ 00:20:39,440 [Speaker 2] So yes, people had heard of computers.
248 00:20:39,440 ⇒ 00:20:43,440 [Speaker 2] But they weren’t using them because when I started using computers,
249 00:20:43,440 ⇒ 00:20:48,440 [Speaker 2] which was about 1960, there were like 10 or 12 computers in New York.
250 00:20:48,440 ⇒ 00:20:51,440 [Speaker 2] And one of which I had access to.
251 00:20:51,440 ⇒ 00:20:56,440 [Speaker 1] Well, you’re a good cat to spend some time with me.
252 00:20:56,440 ⇒ 00:20:57,440 [Speaker 1] I really do appreciate it.
253 00:20:57,440 ⇒ 00:20:58,440 [Speaker 1] My pleasure.
254 00:20:58,440 ⇒ 00:20:59,440 [Speaker 2] Thank you very much.
255 00:20:59,440 ⇒ 00:21:00,440 [Speaker 1] Thank you very much.