SITALWeek #370
Welcome to Stuff I Thought About Last Week, a personal collection of topics on tech, innovation, science, the digital economic transition, the finance industry, and whatever else made me think last week.
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In today’s post: the effort to design new computing platforms from artificial and real neurons has the potential to upend the current processor and software/AI architectures; bundling Hollywood videos into an unending ocean of content; a look at Bob Dylan's new book and his philosophical ins generational shifts and changing technologies; productivity drops the most since 1947 as people change jobs and return to the office.
Stuff about Innovation and Technology
Superconducting Synapse vs. DishBrain
Neuromorphic microchips operate more like neurons than traditional semiconductors. Rather than running and communicating at all times, neuromorphic circuits fire (i.e. transmit) only above certain thresholds of input, as do neurons. One of the largest impediments to AI is that the human brain is so incredibly power efficient that we can’t come close to replicating the performance per watt in silicon. The computational capacity and connectivity of neuromorphic chips have likewise been limited by traditional, power-hungry hardware. Researchers at NIST are working to overcome the baggage of traditional electronics for these neural-mimetic chips by using optical waveguides, which allow each neuron to connect with thousands of others at the speed of light. To bypass the conundrum of having to trap light on a microchip, researchers came up with a novel way to convert photonic signals to 2-picosecond-long electrical pulses: “These pulses each consisted of a single magnetic fluctuation, or fluxon, within a network of superconducting quantum-interference devices, or SQUIDs.” Scientists were surprised at how easy it was to construct this system, which resulted in artificial neurons firing 30,000 times faster with only 0.3% energy usage vs. biological neurons.
A different approach to creating more efficient neuron-like processing is to use actual neurons. Cortical Labs in Australia has trained a dish of mouse neurons, along with neurons grown from human precursor cells, to play the game Pong. The DishBrain consists of a petri dish with a single-layer mesh of 800,000 cells connected to hardware and software, which trains the neurons using feedback. For comparison, the human brain has 86B neurons and a fly brain has 100,000. Rather than the dopamine humans are used to, the cells are rewarded with a predictable signal (and poor performance is anti-rewarded with unpredictable signals). This system of reward sounds eerily similar to Karl Friston’s free energy theory of the brain. I covered Friston in #271 and #272: “This can be viewed as minimizing free energy, which is simply the difference between what you expect to happen and what your bodily senses are telling you is actually happening. For example, if I expect that I will warm up by stepping from shade into sunlight, and then proceed to do so, odds are the temperature receptors in my skin will confirm that prediction – no surprise and minimized free energy. Underlying the free energy principle is the idea that the brain is a Bayesian probability machine...If the brain acts as a Bayesian machine, it will constantly adjust predictions based on new sensory inputs. According to his free energy principle, if the brain makes a prediction that appears incorrect, it can respond in one of two ways: accepting the surprise and modifying its version of the world (Bayesian inference) or by acting to make the prediction true (what Friston calls active inference).” The gist of the theory is that the brain works to minimize energy consumption by making more accurate predictions. The free energy principle might be key to advancing new forms of neural networks, whether mechanical or lab grown. If any of these efforts, using standard inputs, can create usable outputs at scale, they would effectively replace the current ecosystem of AI chips and processors. Also, possibly, the world would be changed so fundamentally that we don’t have the language or imagination to describe what might happen.
YouTube’s Primetime
Bundling is King. That’s what I wrote back in #359, noting that YouTube was in one of the best positions to re-bundle the deconstructed Hollywood content. Last week, YouTube announced it would begin offering content from the studios’ streaming platforms as Primetime Channels, embedded in YouTube’s flagship 2B-user app. I noted back in August that the value in media now lies in the distribution platform that can offer the highest non-zero-sum value bundle and also drive the highest advertising rates for content creators. The biggest issue for Hollywood’s premium content studios is the extreme fragmentation of attention, something I wrote about in more detail in The TikTokification of Consumption Habits. Today’s professional content appears to have diminishing value, a reality that becomes apparent when you compare it to the endless stream of videos on YouTube. While bundling is likely the only path forward for Hollywood, it’s a double edged blade. The algorithm is king now, and Hollywood’s content is as indistinguishable as a drop of water in an endless sea.
Miscellaneous Stuff
Dylan on TikTok?
I was reading this New Yorker article on Bob Dylan and came across his comments on the cumulative nature of creativity, i.e., artists are constantly building on what came before them. The article quotes Dylan from an 2015 acceptance speech saying: “These songs didn’t come out of thin air, I didn’t just make them up out of whole cloth. . . . It all came out of traditional music: traditional folk music, traditional rock and roll, and traditional big-band swing orchestra music. . . . If you sang ‘John Henry’ as many times as me—‘John Henry was a steel-driving man / Died with a hammer in his hand / John Henry said a man ain’t nothin’ but a man / Before I let that steam drill drive me down / I’ll die with that hammer in my hand.’ If you had sung that song as many times as I did, you’d have written ‘How many roads must a man walk down?’ too. All these songs are connected. I just opened up a different door in a different kind of way...I thought I was just extending the line.” This sentiment got me thinking about generative AI and the controversy over algorithms using other artists’ work to create new outputs in music, images, and video.
Dylan has also commented in the past on the progress of technology from generation to generation. Back in SITALWeek #249 (June 2020), I posted Dylan’s response (NYT article) to the reporter asking: “Are you worried that in 2020 we’re past the point of no return? That technology and hyper-industrialization are going to work against human life on Earth?” Dylan replied: “Sure, there’s a lot of reasons to be apprehensive about that. There’s definitely a lot more anxiety and nervousness around now than there used to be. But that only applies to people of a certain age like me and you, Doug. We have a tendency to live in the past, but that’s only us. Youngsters don’t have that tendency. They have no past, so all they know is what they see and hear, and they’ll believe anything. In 20 or 30 years from now, they’ll be at the forefront. When you see somebody that is 10 years old, he’s going to be in control in 20 or 30 years, and he won’t have a clue about the world we knew. Young people who are in their teens now have no memory lane to remember. So it’s probably best to get into that mind-set as soon as we can, because that’s going to be the reality. As far as technology goes, it makes everybody vulnerable. But young people don’t think like that. They could care less. Telecommunications and advanced technology is the world they were born into. Our world is already obsolete.”
I started listening to the audiobook of Bob Dylan's newly released The Philosophy of Modern Song. This fascinating book covers 20th-century American musical history through an examination of 66 songs (66 may or may not be a coincidence – 1966 was the culminating year of a long run of successful releases and performances, followed by a controversial dabbling in new-fangled rock and roll, after which Dylan retreated from the public eye for eight years). The audiobook features Dylan and an entertaining cast of characters, including Jeff Bridges, John Goodman, and Steve Buscemi (as well as a few actors who were not in The Big Lebowski). Discussing the song “My Generation” by The Who, Dylan writes “Today it is commonplace to stream a movie directly to your phone. So, when you are watching Gloria Swanson as faded movie star Norma Desmond proclaim from the palm of your hand ‘I am big, it’s the pictures that got small’, it contains layers of irony that writer/director Billy Wilder could never have imagined. Of course someone streaming something to their phone is most likely watching something shorter and faster-paced on TikTok. Certainly not anything in black and white with a running time of 110 minutes. Every generation gets to pick and choose what they want from the generation that came before with the same arrogance and ego-driven self importance that the previous generations had when they picked the bones of the ones before them.” Is there a connection between Dylan's comments in the NYT on the obsoletion of the older generations and his comments on picking the bones clean in "My Generation"? It seems so. In contemplating these connected concepts of iterative art, generational technology shifts, and Dylan’s seeming awareness of TikTok, I can’t help but chuckle at the idea of Dylan someday becoming wildly popular on the short-form video platform, which itself is a mish mash of people iterating on songs and dances of others. We know that Dylan, in his long career, revels in playing with truth and reality to the point where it’s impossible to know what’s real (see #198). So, perhaps stranger things have happened than Bob Dylan one day shooting to TikTok superstardom. We can only hope.
Stuff about Geopolitics, Economics, and the Finance Industry
Productivity Plunge Risks Stagflation
This year in the US, returning to offices and job hopping has created a precipitous decline in productivity not seen since 1947. It appears that, despite viral reports of mouse jiggling, white collar workers were getting more done at home during the pandemic. And, now, accelerated quits – as people “swipe up” for higher salaries – has slowed output, with experienced employees having to stop what they are doing to train each new crop of temporary-minded hires. This unproductive mire is one of the biggest factors sustaining inflation, as productivity can be one of the best ways to combat rising prices. All of these circumstances should continue to feed an accelerated automation of white and blue collar jobs.
✌️-Brad
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