Often times it is hard to appreciate the work of Nobel Prize winners, including those in Economics. Thaler is not one of those people. His work is very approachable for laypeople, and the benefits of his work is obvious.
Here’s one example, of how his work led to better results for people in terms of pensions.
Youtube is a great source of videos on Thaler. If you want to get started understanding what is behind his thinking, you can start there.
In addition, the New York Times covers his award winning here and it is another good introduction. Finally, here is a piece in the Times that Thaler wrote himself, on the power of Nudges. If you do anything, read that.
Good to see him win.
This piece: What it’s like to be a modern engraver, the most automated job in the United States — Quartz, reminded me once again that the best use of technology is to augment the people doing the work, and not simply take away the work. Must reading for anyone who’s believes that the best way to use AI and other advanced tech is to eliminate jobs. My believe is that the best way to use AI and other advanced tech is to make jobs better, both for the employee, the employer, and the customer. The businesses that will succeed will have that belief as well.
(Image from this piece on how humans and robots can work together.)
According to Haydn Waters, a writer at CBC, the mail robots at the corporation are being discontinued. Instead:
Mail will be delivered twice a week (Tuesday and Thursday) to central mail delivery/pickup locations on each floor.”
What gets lost in alot of discussions of robots, AI, etc., taking all the jobs is that the drivers for the decisions is not technology but economics. If there is no economical need for robots and other technology, then that technology will not just appear. There is nothing inevitable about technology, and any specific technology is temporary.
Of course there will be more use of robots and AI and other technology to replace the work people may currently do. The key to finding work will be to continually improvise and improve on the tasks one has to do to remain employed. That’s something humans do well, and technology will struggle with for some time in the future, AI hype not withstanding.
If you are looking to build AI tech, or just learn about it, then you will find these interesting:
- Artificial intelligence pioneer says we need to start over – Axios – if Hinton says it, it is worth taking note
- Robots Will Take Fast-Food Jobs, But Not Because of Minimum Wage Hikes | Inverse – true. Economists need to stop making such a strong link here.
- Artificial Intelligence 101: How to Get Started | HackerEarth Blog – a good 101 piece
- Deep Learning Machine Teaches Itself Chess in 72 Hours, Plays at International Master Level – MIT Technology Review – the ability of tech to learn is accelerating.
- Now AI Machines Are Learning to Understand Stories – MIT Technology Review – and not just accelerating, but getting deeper.
- Robots are coming for your job. That might not be bad news – good alternative insight from Laurie Penny.
- Pocket: Physicists Unleash AI to Devise Unthinkable Experiments – not surprisingly, a smart use of AI
- AI’s dueling definitions – O’Reilly Media – this highlights one of the problems with AI, and that it is it is a suitcase word (or term) and people fill it with what they want to fill it with
- A Neural Network Playground – a very nice tool to start working with AI
- Foxconn replaces ‘60,000 factory workers with robots’ – BBC News – there is no doubt in places like Foxconn, robots are taking jobs.
- 7 Steps to Mastering Machine Learning With Python – don’t be put off by this site’s design: there is good stuff here
- How Amazon Triggered a Robot Arms Race – Bloomberg – Amazon made a smart move with that acquisition and it is paying off
- When Police Use Robots to Kill People – Bloomberg this is a real moral quandary and I am certain the police aren’t the only people to be deciding on it. See also: A conversation on the ethics of Dallas police’s bomb robot – The Verge
- How to build and run your first deep learning network – O’Reilly Media – more good stuff on ML/DL/AI
- This expert thinks robots aren’t going to destroy many jobs. And that’s a problem. | The new new economy – another alternative take on robots and jobs
- Neural Evolution – Building a natural selection process with AI – more tutorials
- Uber Parking Lot Patrolled By Security Robot | Popular Science – not too long after this, one of these robots drowned in a pool in a mall. Technology: it’s not easy 🙂
- A Robot That Harms: When Machines Make Life Or Death Decisions : All Tech Considered : NPR – this is kinda dumb, but worth a quick read.
- Mathematics of Machine Learning | Mathematics | MIT OpenCourseWare – if you have the math skills, this looks promising
- Small Prolog | Managing organized complexity – I will always remain an AI/Prolog fan, so I am including this link.
- TensorKart: self-driving MarioKart with TensorFlow – a very cool application
- AI Software Learns to Make AI Software – MIT Technology Review – there is less here than it appears, but still worth reviewing
- How to Beat the Robots – The New York Times – meh. I think people need to learn to work with the technology, not try to defeat it. If you disagree, read this.
- People want to know: Why are there no good bots? – bot makers, take note.
- Noahpinion: Robuts takin’ jerbs
- globalinequality: Robotics or fascination with anthropomorphism – everyone is writing about robots and jobs, it seems.
- Valohai – more ML tools
- Seth’s Blog: 23 things artificially intelligent computers can do better/faster/cheaper than you can – like I said, everyone is writing about AI. Even Seth Godin.
- The Six Main Stories, As Identified by a Computer – The Atlantic – again, not a big deal, but interesting.
- A poet does TensorFlow – O’Reilly Media – artists will always experiment with new mediums
- How to train your own Object Detector with TensorFlow’s Object Detector API – more good tooling.
- Rise of the machines – the best – by far! – non-technical piece I have read about AI and robots.
- We Trained A Computer To Search For Hidden Spy Planes. This Is What It Found. – I was super impressed what Buzzfeed did here.
- The Best Machine Learning Resources – Machine Learning for Humans – Medium – tons of good resources here.
Economists write a lot about the mystery of why productivity is not increasing, with pieces such as this. There’s even a section on it in Wikipedia.
My own theory is that limited wage increases is also limiting the benefits of productivity aids. How I think this works is so:
- Employers wont raise wages for employees.
- Employers deploy technology that should result in productivity gains.
- Employees take the technology deployed and use them to decrease their efforts.
- The employer sees some productivity gains and assumes that is the limit for the technology deployed.
Look at this chart:
In much of the world economy, all the job growth is in the services sector (green line), not the manufacturing sector (red line). Achieving productivity gains in the manufacturing sector is more straightforward: replace people with robots and you are done. It’s not as straightforward as that in the services sector. In some services sector jobs, it is not possible to decrease effort without it being visible. But in many services sector jobs, it is. If employees cannot improve their lives by making more money, they may decide to do so by working less and working right up to the point where they don’t lose their job.
If you look at employment as a game, then we currently have a Nash equilibrium where the employees know that they won’t get paid more working for the same company, because that is the best strategy for the company. Therefore the best strategy for the employee is to minimize their effort without getting fired and while showing little if any productivity gains.
That’s to me is key reason why I think we have the productivity paradox.
I would add that the reason this is a paradox is because no one wants to admit that this is happening. It seems like a failure on both the employers and the employees side. The employee wants to be seen as a good worker and the employer doesn’t want to admit it could be paying more. Instead technology is brought in to solve an organizational problem, which is something technology cannot do.
(Chart from Business Insider).
It is striking to see what percentage of American capital attributed to slavery in the 18th and 19th centuries (the striped section in the chart above). In the late 18th century only agricultural land counted for more, and there slavery contributed to that too.
The American Civil War and the emancipation of those bound in slavery destroyed all that capital, and that was great and necessary. While it is wrong to consider slavery only in terms of money, it is impossible to talk about slavery in the United States without considering its relationship to the economy and capital. The capital that derived from slavery was massive.
In the U.K. the abolition of slavery resulted in the government providing capital back to the slave owners. It was a terrible omission that neither the U.K. nor the U.S. provided capital to the freed slaves. There are those, like Ta-Nehisi Coates, who argue that such capital in the form of reparation is due. Based on the chart above, a case could be made that it would be a tremendous amount of money.
(Chart above taken from “Capital in the Twenty-First Century” by Thomas Piketty)
There have been many articles written on UBI. (If you don’t know what it is, it’s universal basic income: a cash payment made to every individual in the country).
Two of the more interesting ones I’ve read are here: The UBI already exists for the 1% – Medium, and this one here (on how India is looking to do it).
UBI is coming. It may take some time though.