I do understand that slide decks are often the main vehicle for venture capital and other pitches, but my own idiosyncratic take is that they obscure more than they illuminate. I would much rather see the person try to write out a half-page explanation of the plan, so I can better see if one thought follows from the next, and if one step follows from the next. I learn much better how the person actually thinks. The separate slide-by-slide, horizontal juxtaposition of the slide deck does not (for me) perform that function very well. It can too easily hide structure, or the lack thereof.
So in my view writing is (still) underrated.
Mr. Gold, who is short and stocky, with the good-natured ease of a standup comedian, does his chores while carrying a digital camera in one hand and murmuring into a microphone.
Then, twice a week, like clockwork, he posts a short video on YouTube about his exploits as a neophyte farmer, often highlighting failures or pratfalls. Keeping a close eye on analytics, he has boosted his YouTube audiences high enough to provide a steady advertising revenue of around $2,500 to $4,000 a month, about eight times what he earns from selling farm products.
It’s a lot of work: Mr. Lumnah wakes up at 3:30 a.m. so he can edit the previous day’s footage in time to post new video at 6 a.m., which his 210,000 regular viewers, who are scattered as far as Cambodia and India, have come to expect. “People will say, it’s lunchtime here in Ukraine,” Mr. Lumnah said.
Others, like Justin Rhodes, a farmer in North Carolina, have parlayed a giant YouTube audience into a dues-paying membership enterprise — he has 2,000 fans who pay annual fees of up to $249 for private instruction and direct communication, via text message. “We don’t sell a single farm product,” Mr. Rhodes said. “Our farm product is education and entertainment.”
Some of them earn money through product endorsement deals, like Al Lumnah, who posts videos five days a week from his farm in Littleton, N.H.
Here is more from the NYT, via Steve Rossi.
The moonshot goal of this project is to build a reinforcement learning framework that will recommend economic policies that drive social outcomes in the real world, such as improving sustainability, productivity, and equality. To achieve this, we’ll need to advance AI, challenge conventional economic thinking, and create AI that can ground and guide policy making. While none of these tasks are easy, together, they make for a true moonshot.
This moonshot is both ambitious and necessary, and more timely than ever given economic challenges around the world. Importantly, the AI Economist is a powerful optimization framework that can objectively automate policy design and evaluation. This will allow economists and policy experts to focus on the end goal of improving social welfare.
Given the social and ethical implications that economic policies can have, we believe it is essential to have transparency in the process. By open sourcing the AI Economist, not only do we empower collaboration from all over the world but we also enable unfettered review of policy simulations.
The key ingredients are:
A high-fidelity simulation that should be grounded in data, and aligned with economic theory as well as with social and ethical values. Simulations should not be prohibitively expensive to run, and should be maintainable and modular.
AI policy models should be effective in a wide range of scenarios, explainable, and robust to economic shocks.
The simulation and policy models should be calibrated against real-world data and, as much as possible, validated in human-subject studies.
I suppose I am skeptical, but fortunately progress does not depend on pleasing me. There is much more information at the original link.
For the pointer I thank Mike Doherty.
Excellent and interesting throughout, here is the transcript, video, and audio. Here is part of the summary:
He joined Tyler for a conversation about which areas of science are making progress, the factors that have made research more expensive, why government should invest more in R&D, how lean management transformed manufacturing, how India’s congested legal system inhibits economic development, the effects of technology on Scottish football hooliganism, why firms thrive in China, how weak legal systems incentivize nepotism, why he’s not worried about the effects of remote work on American productivity (in the short-term), the drawbacks of elite graduate programs, how his first “academic love” shapes his work today, the benefits of working with co-authors, why he prefers periodicals and podcasts to reading books, and more.
Here is an excerpt:
COWEN: If I understand your estimates correctly, efficacy per researcher, as you measure it, is falling by about 5 percent a year [paper here]. That seems phenomenally high. What’s the mechanism that could account for such a rapid decline?
BLOOM: The big picture — just to make sure everyone’s on the same page — is, if you look in the US, productivity growth . . . In fact, I could go back a lot further. It’s interesting — you go much further, and you think of European and North American history. In the UK that has better data, there was very, very little productivity growth until the Industrial Revolution. Literally, from the time the Romans left in whatever, roughly 100 AD, until 1750, technological progress was very slow.
Sure, the British were more advanced at that point, but not dramatically. The estimates were like 0.1 percent a year, so very low. Then the Industrial Revolution starts, and it starts to speed up and speed up and speed up. And technological progress, in terms of productivity growth, peaks in the 1950s at something like 3 to 4 percent a year, and then it’s been falling ever since.
Then you ask that rate of fall — it’s 5 percent, roughly. It would have fallen if we held inputs constant. The one thing that’s been offsetting that fall in the rate of progress is we’ve put more and more resources into it. Again, if you think of the US, the number of research universities has exploded, the number of firms having research labs.
Thomas Edison, for example, was the first lab about 100 years ago, but post–World War II, most large American companies have been pushing huge amounts of cash into R&D. But despite all of that increase in inputs, actually, productivity growth has been slowing over the last 50 years. That’s the sense in which it’s harder and harder to find new ideas. We’re putting more inputs into labs, but actually productivity growth is falling.
COWEN: Let’s say paperwork for researchers is increasing, bureaucratization is increasing. How do we get that to be negative 5 percent a year as an effect? Is it that we’re throwing kryptonite at our top people? Your productivity is not declining 5 percent a year, or is it? COVID aside.
BLOOM: COVID aside. Yeah, it’s hard to tell your own productivity. Oddly enough, I always feel like, “Ah, you know, the stuff that I did before was better research ideas.” And then something comes along. I’d say personally, it’s very stochastic. I find it very hard to predict it. Increasingly, it comes from working with basically great, and often younger, coauthors.
Why is it happening at the aggregate level? I think there are three reasons going on. One is actually come back to Ben Jones, who had an important paper, which is called, I believe, “[Death of the] Renaissance Man.” This came out 15 years ago or something. The idea was, it takes longer and longer for us to train.
Just in economics — when I first started in economics, it was standard to do a four-year PhD. It’s now a six-year PhD, plus many of the PhD students have done a pre-doc, so they’ve done an extra two years. We’re taking three or four years longer just to get to the research frontier. There’s so much more knowledge before us, it just takes longer to train up. That’s one story.
A second story I’ve heard is, research is getting more complicated. I remember I sat down with a former CEO of SRI, Stanford Research Institute, which is a big research lab out here that’s done many things. For example, Siri came out of SRI. He said, “Increasingly it’s interdisciplinary teams now.”
It used to be you’d have one or two scientists could come up with great ideas. Now, you’re having to combine a couple. I can’t remember if he said for Siri, but he said there are three or four different research groups in SRI that were being pulled together to do that. That of course makes it more expensive. And when you think of biogenetics, combining biology and genetics, or bioengineering, there’s many more cross-field areas.
Then finally, as you say, I suspect regulation costs, various other factors are making it harder to undertake research. A lot of that’s probably good. I’d have to look at individual regulations. Health and safety, for example, is probably a good idea, but in the same way, that is almost certainly making it more expensive to run labs…
COWEN: What if I argued none of those are the central factors because, if those were true as the central factors, you would expect the wages of scientists, especially in the private sector, to be declining, say by 5 percent a year. But they’re not declining. They’re mostly going up.
Doesn’t the explanation have to be that scientific efforts used to be devoted to public goods much more, and now they’re being devoted to private goods? That’s the only explanation that’s consistent with rising wages for science but a declining social output from her research, her scientific productivity.
COWEN: What exactly is the value of management consultants? Because to many outsiders, it appears absurd that these not-so-well-trained young people come in. They tell companies what to do. Sometimes it’s even called fraudulent if they command high returns. How does this work? What’s the value added?
The second is:
Furthermore, the tech companies have been rising in popularity. I am going to “call” that the “war against Big Tech” essentially is over, and that the critics have failed. The new debate will be about ensuring universal access to various internet services (which will involve further regulation of some kind), not splitting up the major companies or eliminating their basic functions. You might also try this National Journal headline:
It is striking just how much that “blockbuster tech hearing” has not become an enduring story for people to talk about.
Praveen Mishra when he was 16 started the Power of Youth, a non-profit aimed at empowering rural students by giving them mentorship and conducting competitions to highlight their potential. He since has been building a ‘YouTube of e-commerce’. He is the founder of ByBuy, an omni-channel retail platform, and he received his EV grant to help with this launch.
Akash Bhatia and Puru Botla
Akash and Puru are the co-founders of Infinite Analytics (IA), a Boston-based company whose proprietary AI platform analyzes customers’ data. They received their EV grant to repurpose their platform for Covid containment to help governments and authorities in India with contact tracing and mobility analyses. They have since helped millions of users, and their Containment Zone analyses are becoming the bedrock for lockdown exit strategy in Mumbai and Pune. Here is a video about the project.
Mohammed Suhail Chinya Salimpasha
Suhail is a 19-year-old senior grade homeschooler. He dropped out of high school to work on finding new ways to quantify protein in serum applied on a faster diagnosis of malnutrition. This is his TedX talk on the project. He diverted his efforts towards Covid, to create India’s first multi-language Covid symptom checker, which was adopted by some local authorities before the Government mandated an alternative. He is currently working on solving problems in containerizing applications, Enterprise Cloud, low latency API communication, and 5G In Social Tech Democratization.
Manasseh John Wesley
Manasseh John Wesley is a 21-year-old from Hyderabad, India, studying engineering and technologies like embedded systems megatronics/machine learning/data science/digital communication systems. He is the founder of River Bend Data Solution, a data science company with health care applications. He received an EV grant to create a platform for hospitals to provide X-rays and CT scan images and to use AIML to identify at risk districts in Andhra Pradesh.
Vidya Mahambare and Sowmya Dhanaraj
Dr. Vidya Mahambare is a Professor of Economics at Great Lakes Institute of Management working in macroeconomics as well as cultural and social economics issues. Dr. Soumya Dhanaraj is an assistant professor of economics at the Madras School of Economics, working in Development Economics and Applied Microeconomics. Their grant is to support their work in labor market and migration distortions.
Onkar Singh Batra is a fourteen-year-old web developer from Jammu and Kashmir. He developed and published his first website at the age of seven and holds the record for the World’s Youngest Webmaster. Furthermore, his book ‘When the Time Stops’ made him hold the record for the record of ‘World’s Youngest Theoretical Author.’ Recently, responding to the Covid pandemic, he received his EV grant for the web applications named –‘COVID Care Jammu’ and ‘COVID Global Care’, which connects doctors with users and helps users do a free anonymous Covid Risk Assessment test. Onkar built his website keeping in mind slow internet speed and limited access. He has plans for many future projects, including working on a bio shield for 5G radiation technology.
Nilay Kulkarni is a 20-year old software developer and he previously worked on a project to prevent human stampedes at the world’s largest gathering – the Kumbh Mela. His project’s implementation at the 2015 edition of the event in Nashik, with over 30 million attendees, led to the first stampede-free Kumbh Mela in the city’s history. Nilay has also spoken at TEDx New York about the project. He has worked on assistive technology for people with ALS enabling them to control phones using their tongues. He received his EV grant for the tech development of the MahaKavach App, the official quarantine monitoring and contact tracing platform adopted by the state government of Maharashtra. So far, the platform has helped reduce the time needed for contact-tracing from 3-4 days to 25-30 minutes, and he is now working on open-sourcing the platform for greater impact.
Data Development Lab
Drs. Paul Novosad and Sam Asher are previous EV grantees for creating the SHRUG database at Data Development Lab. The SHRUG is an ultra-clean geocoded database describing hundreds of dimensions of socioeconomic status across 8,000 towns and 500,000 villages in India. Everything in the SHRUG is carefully linked, extensively vetted and documented, and ready for immediate application. In addition to continually expanding the SHRUG, they recently received another EV grant for a second platform oriented toward informing the COVID-19 response in India. This platform has a wealth of linked pandemic-related data (e.g. hospital capacity, health system use, agricultural prices) not available anywhere else and is directly feeding several COVID response research and policy teams.
Deepak VS is a 23-year-old Mechatronics Engineer from Bangalore, India and he has worked on traffic and communications projects. He also founded a college club called 42 Labs that eventually grew into a startup company called Tilt, a shared mobility platform designed for Indian campuses but now in corporate parks, colleges, townships, and cities across India. Working primarily with electric bikes, Tilt is partnering with companies to help provide alternate mobility solutions to people who typically use crowded and unsafe public transport.
Amit Varma and Vivek Kaul
Amit Varma is one of the most influential podcasters in India, and the winner of the Bastiat Prize in Journalism for his writing. He is the host of the iconic longform interview podcast The Seen and the Unseen, my chat with him on Stubborn Attachments is here and Alex’s appearances on the show here and here. Vivek Kaul is a prominent journalist and writer covering finance and economics. His most recent book, “Bad Money: Inside the NPA Mess and How It Threatens the Indian Banking System” was released earlier this month.
Raman Bahl is a 2012 Teach For India Fellow. He has worked over the last decade in different capacities to teach students, train teachers, create curricula, and create systems of teaching and learning in the Indian education system. In the light of the pandemic, rural communities in India are not getting access to quality learning at home. In particular, students from poorer and marginalized groups cannot access to remote/online education launched by local schools because they lack internet access, televisions, and/or learning materials. Raman received his EV grant for creating a Voice-based Academic System for students in rural communities, to enable access to learning at home, through mobile phones. He is launching the system in Purkhas Rathi in Haryana and hopes to scale the system to more villages and states.
Vidyarthi Baddireddy, Utsav Bhattacharya and Kajal Malik are Indian entrepreneurs focused on the employability of graduating students in India. In 2017 they founded Reculta to digitize campus placements. In 2019, they launched PickMyWork, a platform for onboarding gig workers and getting them to complete tasks for client organizations through a pay-per-task model. In light of the manpower crisis during the Covid pandemic, especially on the frontlines, they want to enable matching of volunteers to emergency situations. They received their EV grant for adapting PickMyWork as a local volunteer response system to emergency situations like Covid by using the platform to source, train and deploy volunteers across various projects and locations.
Harsh Patel and Hiten Patel
Harsh Patel is an undergraduate student in electronics and communication engineering; his interests are in components, coding, and robotics. Hiten Patel is an electrical engineer interested in robotics, coding, and designing. They received their EV grant to develop robot prototypes that they call ‘E-Bot: Arogya Sahayak’ to potentially support hospitals, hotels, airports, workplaces, etc., to assist with basic tasks while maintaining social distancing.
Vinay Débrou studied computer science and is a self-taught data scientist interested in psychology, data science, and new applications of network science for collaboration-generating contexts. He has also built resources for aspiring location-independent free-agents including a curated resources library and a weekly newsletter. Vinay received his Emergent Ventures grant to accelerate his ongoing project to build a network visualization/mapping tool (v0.1 here) to catalyze cross-disciplinary expertise-sharing and collaboration in Yak Collective – an open, networked community of 300+ (and growing) independent creators, consultants, and researchers.
Those unfamiliar with Emergent Ventures can learn more here and here. EV India announcement here. To apply for EV India, use the EV application click the “Apply Now” button and select India from the “My Project Will Affect” drop-down menu.
If you are interested in supporting the India tranche of Emergent Ventures, please write to me or to Shruti at [email protected] I believe we are seeing a blossoming of talent from India comparable to that from Central Europe in the early part of the 20th century.
From an email from Agustin Lebron, noting that I will impose no further indentation:
“One thing that’s worth noting:
The degree of excitement about GPT-3 as a replacement for human workers, or as a path to AGI, is strongly inversely correlated with:
(a) How close the person is to the actual work. If you look at the tweets from Altman, Sutskever and Brockman, they’re pumping the brakes pretty hard on expectations.
(b) How much the person has actually built ML systems.
It’s a towering achievement to be able to train a system this big. But to me it’s clearly a dead-end on the way to AGI:
– The architecture itself is 3 years old: https://arxiv.org/abs/1706.03762. It is not an exaggeration to say that GPT-3’s architecture can be described as “take that 2017 paper and make 3 numbers (width, # layers, # heads) much bigger”. The fact that there hasn’t been any improvement in architecture in 3 years is quite telling.
– In the paper itself, the authors clearly say they’re quite near fundamental limits in being able to train an architecture like this. GPT-3 isn’t a starting point, it’s an end-point.
– If you look at more sober assessments (http://lacker.io/ai/2020/07/06/giving-gpt-3-a-turing-test.html, https://minimaxir.com/2020/07/gpt3-expectations/), without the tweet selection bias, it starts to look less impressive.
– Within my fairly heterogeneous circle of ML-expert friends, there’s little disagreement about dead-end-ness.
The most interesting thing about GPT-3 is the attention and press that it’s gotten. I’m still not sure what to make of that, but it’s very notable.
Again, it’s incredibly impressive and also piles of fun, but I’m willing to longbet some decent money that we’re not replacing front-end devs with attention-layer-stacks anytime soon.”
I remain bullish, but it is always worth considering other opinions.
Here is an email from a reader, I do not really have an opinion of my own yet. Please note I will not indent any further:
“I wanted to draw your attention to something. Are you familiar with “AI Dungeon,” text-based RPG “open world” game running on GPT-2 / GPT-3? Here’s the author’s discussion on medium, or you can play the GPT-2 version for free to get a sense of it directly.
But what I really want to draw your attention to is players who are using custom prompts to open up dialogs with GPT-3 about non-game things.
This result is particularly impressive: https://www.reddit.com/r/slatestarcodex/comments/hrx2id/a_collection_of_amazing_things_gpt3_has_done/fy7i7im/
If the author is to be believed, they’ve had GPT-3 / “Dragon”:
1. write code
2. act as a pharmacology tutor
3. write poetry
4. translate english, french, chinese (the instruction to “balance the intent of the author with artistic liberty” is particularly interesting)
It’s hard to excerpt, I’d recommend reading the whole thing if you have time.
Here’s another user’s eloquent conversation about the experience of being an AI, using a similar mechanism (screencap images of the convo, part 1 and part 2 ), with a sample prompt if you want to converse with GPT-3 yourself via AI Dungeon.
I am increasingly convinced that Scott Alexander was right that NLP and human language might boostrap a general intelligence. A rough criteria for AGI might be something like (i) pass the Turing test, and (ii) solve general problems; the GPT-3-AI-Dungeon examples above appear to accomplish preliminary versions of both.
GPT was published in June 2018, GPT-2 in February 2019, GPT-3 in May 2020.
As best I can tell GPT -> GPT2 was ~10x increase in parameters over ~8 months, and GPT2 -> GPT3 was ~100x increase of parameters over ~14 months. Any number of naive projections puts a much more powerful release happening over the next ~1-2yrs, and I also know that GPT-3 isn’t necessarily the most powerful NLP AI (perhaps rather the most popularly known.)
When future AI textbooks are written, I could easily imagine them citing 2020 or 2021 as years when preliminary AGI first emerged,. This is very different than my own previous personal forecasts for AGI emerging in something like 20-50 years…
p.s. One of the users above notes that AI Dungeon GPT-3 (“Dragon”) is a subscription service, something like ~$6 a week. MIE.”
I will be doing a Conversation with him, based in part on his new forthcoming book One Billion Americans: The Case for Thinking Bigger. While I have not yet read it, I strongly expect it will be excellent.
So what should I ask?
That is the topic of my latest Bloomberg column. I suggest it will take three major forms, namely anti-China, pro-internet as a communications medium (as an offset to left-wing media), and dislike of the Left, most of all the latter. Note these are predictions rather than normative claims about what should happen. Here is one excerpt:
Last and perhaps most significant, the intellectual right will dislike the left. It pretty much does already, but the antagonism will grow. Opposition to political correctness and cancel culture, at least in their left-wing versions, will become the most important defining view. As my colleague Bryan Caplan succinctly put it four years ago: “Leftists are anti-market. … Rightists are anti-leftist.”
The intensity of this dislike will mean that, within right-wing circles, free speech will prosper. As long as you take care to signal your dislike of the left, you will be allowed to hold many other heterodox views without being purged or penalized.
If you are on the Left, note that it does not suffice to dislike the Right, you have to dislike most parts of the Left as well (why is that? Can you model this?).
I also consider social conservatism, libertarianism, communitarianism, and Sam’s Club Republicanism as possible alternative directions for the intellectual Right. The entire column repays careful study.
On first read, that sentiment might seem banal. Of course we should clearly oppose China’s intensifying political repression. But is easier to list American business leaders who have cravenly excused the inexcusable than to name those such as Collison, who have been brave enough to state the obvious. When it comes to China’s human rights abuses, the position of the American business community is prone…
“It must be possible,” Collison tells me, “to acknowledge the basic facts — for example, that concentration camps and forced sterilization programs are reprehensible evils. If it becomes de facto unacceptable to do so, as part of some kind of self-perpetuating silence, it really seems to me that that’s a positive feedback loop that we should hurry to break.”
There is much more at the link, definitely recommended.
It has gone great so far, but I don’t think it is socially optimal to be doing this forever. Here is my latest Bloomberg column on that topic, 2x the usual length, excerpt:
If Twitter, Facebook and other tech companies shift toward everyone working from home, it will mean less reliance on esprit de corps and morale to ensure performance, and more management using direct financial incentives and project- and output-based monitoring. Virtual tools can help organize teams, but they simply can’t replicate the intellectual frisson of “gathering the smart people” together, and this could damage performance and innovation.
There is some evidence that when employees work at a distance, they don’t put in extra hours or extend themselves for the benefit of co-workers. That probably means a better work-life balance for many people, but perhaps also inferior performance from a lot of companies over the longer haul.
This move away from workplace morale as a motivator will help self-starter employees, but it may not be good for tech labor overall. In essence, without a local workplace ethos, it is easier to commoditize labor, view workers as interchangeable and fire people. The distinction between protected full-time employees and outsourced, freelance and contract workers weakens. A company can make the offer of, “If you hand in your project, we pay you,” to virtually any worker around the world, many of whom might accept lower wages for remote roles.
Bringing new workers on board is an especially difficult problem for this model. In the short run, of course, that is a minor concern but over time it grows.
25 simple job features explain over half the variance in which jobs are how automated.
The strongest job automation predictor is: Pace Determined By Speed Of Equipment.
Which job features predict job automation how did not change from 1999 to 2019.
Jobs that get more automated do not on average change in pay or employment.
Labor markets change more often due to changes in demand, relative to supply.
That is all from their newly published paper on the topic.
I am happy to announce two further winners of the Emergent Ventures prizes to fight Covid-19.
The first is to Statnews.com for their excellent and intelligent reporting on public health, including the coronavirus, with the latter articles being ungated.
This is not only a prize for past achievement, but also resources to allow them to continue into the future. As most of you know, journalism is a highly precarious enterprise these days.
And to be clear, this is a one-time prize and it involves absolutely no editorial control or influence over what they publish.
Here is a recent NYT article on Statnews.com. the headline reads: “The Medical News Site that Saw the Coronavirus Coming Months Ago.”
The second winner is Tina White and Covid Watch, for their work on track and trace apps, you will note that Tina and her group were earlier winners of a (smaller) Emergent Ventures fellowship. This is an Early Response prize, for their critical and timely work to boost the quality of these apps and to make them more privacy-friendly and more palatable to civil liberties concerns. Here is some coverage:
Quick update on Curative, the COVID19 testing co – they are currently running 6% of entire US COVID19 testing capacity – from being a sepsis co six weeks ago
— Celine Halioua (@celinehalioua) May 15, 2020
Emergent Ventures is pleased to have been their very first funder, and to have consummated the entire grant process, including the wire of funds (at the time critical for materials purchase), in less than 24 hours.