Social Media isn't just a reflection of human nature
Social media isn't just a reflection of human nature. It's a force that defines human nature, through incentives baked into the way products are designed.
The title is from the book No Filter. Where the author argues that Instagram not a neutral piece of technology. But a tool that provides incentives to users to use the product in a certain way. This line reminds me of Neil Postman’s Amusing Ourselves to Death. Where Neil Postman argued that a piece of media changes how the user sees the world. In the book, he had the example of television. In which TV is a visual medium. So things are done on TV. Where done with the express purpose to entertain. Things on TV are supposed to be visually nice or shocking. Like good looking TV presenters. Or explosions in movies.
This reminds me of a YouTube video I watched. Talking about beautiful people who dominate the music industry. And less good-looking people are locked out. Even though they may be more talented. The Youtuber explained before music videos. Most musicians did not conform to social standards of beauty at the time. Also, they tended to be rock and punk bands. Which tended to be anti-establishment.
But music videos started to be introduced with MTV. A lot more production started to be put into music videos. So storylines inside the music videos. Stunts. And whatnot. After that pretty soon the music directors and industry men. Worked out if they added attractive people on the front of the cover. Then the music started to do well. So then the process started. Where good looking people. Men or female were chosen. To do music videos. And music in general. (This affected females more than males I might add.)
This is why if you look at the top acts right now. Especially female. They tend to be good looking. A good YouTube comment in the video said. “You don’t see any ugly female musicians but you may see male ones”. So you could argue there is some sexism baked into this system. Where a female is more judged on her appearance compared to her male counterparts.
Back to the title at hand. Social media changes human nature because of the incentives. Like the TV example, I gave earlier. People will start to morph their behaviour to fit the mould of the medium. Instagram is a place to show off social status. So people will do things that look like they have a high social status. Like showing off wealth. Travelling around the world. Being beautiful. Instagram is designed to be visual. So people put in a lot of work editing photos. Making sure the backdrop is good. The lighting in the photo is good. To have a great photo.
YouTube is a place where watch time. Is rated highly. So people do around 20-minute videos. With a few emotion spikes here and there. To keep the watcher hooked. Due to YouTube’s design. You choose from a selection of tons of videos. So YouTubers have eye-catching thumbnail and title. To make the user click onto the video.
As people try to please the various algorithms of these social media sites. They become less of a neutral force. And more of a way that the services push people into one direction. To view content. How Neil postman explained with the TV. Which people started to fit content for TV. More focus on visuals and entertainment. Rather than detail and thoroughness.
For example, on Twitter. You can’t write essays on there. So short statements are necessary. In some ways short statements are good. As they force the writer to compress their thoughts. Into its most bare components. One of the advantages. Smart people distil books worth of knowledge into a tweet. Think of James Clear and Naval.
But if you want to bring attention to yourself. Outrage is the way to go. Due to Twitter’s design. People can share and comment very easily. Making the tweet go viral. Outrage works because it’s hard to make nuanced statements. Due to the 240-character limit. So its easier to make a statement thats not true. And let people fill in the gaps. Or try to correct you. This is why many Twitter users say you want to add a spelling mistake to your tweets. So people comment on your post. When they try to correct you. Because outrage is so helpful. Therefore, Twitter can be known to have a bad culture around it. As people use these tricks to bait people into talking about them.
This is not to say outrage is only Twitter. I think I talked about this issue in other places. YouTube for the longest time. And issues of conspiracy theory videos hitting the suggestion feed. Facebook still has an issue with outrage content.
But while they have their issues. I would call these a negative force. The social media services I say are a net positive. But users do need to take extra actions to make social media a net positive. Digital minimalism by Cal Newport talks about this. Some services like YouTube are highly on the net positive side. But has issues like being a serious tool for distraction. I’m fully convinced that social media is not all negative. Contrary to what people think.
People say Twitter is a great networking tool. People use Facebook to keep up with friends and family. Instagram is a great way to show off your hobbies. You can argue that they may better ways to keep up with friends and family compared to Facebook. But it does the job.
But I think we should avoid moral panic. Yes, these tools have issues, and they need to be fixed. But we should put some responsibilities on the individual to use them correctly.
Like disabling notifications. Having limited times on their apps. If they have an issue with outrage bait. Unsubscribe to any content that produces and shares that content.
I learned that if we are more mindful on how to use social media. The experience becomes a lot more pleasant.
Machine learning needed for a moon base
This blog post is going to be slightly off the beaten path. It is still in the realm of technology and science. Which is the stuff I normally write about. But I decided to put a twist on it. As recently I was watching a few videos about space. Namely how we would be building a moon base. I wondered how AI can fit into the mix. So this blog post is asking that question.
Before we land humans on the moon. We need to find sites that are suitable for human occupation. Luckily we are a ready doing this. With satellites and rovers. Scanning the surface of the moon. Areas that will be nice to humans and places close to water. Areas that are suitable to be terraformed. The water is important because we humans need water to live. And taking water from earth to the moon. increases the price of the rocket launch because of the extra weight. Also, water can be used to make rocket fuel on the moon. By extracting hydrogen and oxygen. This reduces the cost of the rocket launch.
The satellites do some type of radar and other measurements on the surface. Looking at different signals. Certain type of results will be water. (Or ice. As the moon is pretty cold.) As it gets that data. Humans on earth can map that data on to a map of the moon. Maybe ML can speed up that process. by having the moon mapped beforehand. And using coordinates to plot the water spot onto the map. Some results are better than others. The ML model can have a probability score on how likely there is water there. Which can help space agencies plan future missions. As they can pick a spot with the most likely chance of water.
Most likely rovers may scout an area before humans land there. So AI will be involved. Like curiosity, it may come with pre-loaded instructions. Probably check if the ground is suitable. For 3D printing. As there is a need to use the materials on the moon. To create the moon base. Verify if there is water in the area. Track how much radiation hits the area. Tons of stuff to make sure humans are safe when they land.
People say that robots may build most of the moon base before humans land. Which makes sense. So other robots will be doing the 3D printing. And fetching supplies like water. So when humans land they have a place to sleep in. That is safe. The robot will probably still be there doing the work. Of creating more buildings for the moon base. Or creating other things like bricks. Humans I guess will start work on making the moon base useable. Like adding wiring and lights. Setting up the internet. And setting up a vertical farm. So they have fresh food to eat.
Personal datastores
There has been talk about having people owning their data. The same data used by tech companies. This sounds like an interesting idea. After reading in The Economist that they maybe EU internal markets for data. In which EU citizens can choose where their data should be stored. So if a German person wants their data stored in France they can. So with the increasing popularity of governments wanting to regulate tech companies. Ownership of one’s data may be on the table. I’m going to be honest, I haven't read too much on the issue. So I'm just spitballing.
Personal datastores can be very useful. Especially for interoperability. Which means users can move their data to different competitors. EU has been thinking of mandating something like this. But it’s difficult because what data should be used for what. I think have written about this before somewhere. But ticktock is different from Facebook. Youtube is different from Twitter. So the data being moved is not one to one nor the same. So how would one fix that? I don’t know.
I think allowing data to be exported. Is something future tech companies will find useful. As they can design their products to take advantage of the feature.
Also, a lot of data of you is contained in third party companies. People like data brokers. And other advertising companies. How would you get data from them? As they don’t have a website that you can access information about you. As they come in pieces. As they used by the tech companies to add them all together to get a whole picture of you. This makes it very difficult. To collect all the data of you out there.
Also, while I think having a datastore is a great idea. Most people frankly won't care about it. As they just want to watch good YouTube videos. And see good memes on Facebook. But I do think people should have their choice on how their data is used. And if they can transfer it to other services. As we the ones being monetised. So it should make sense. That we should have more control over our information. And maybe be compensated for are data. Maybe this can change some of the incentives for tech companies. The company may feel less of a need to hover up data. Or move to a business model were using data from adverts are not useful.
If the EU does go ahead with this. Hope they can write the laws correctly. To avoid bad unintended consequences. And not hurt the smaller tech companies. As the EU. Focuses on mainly curbing American tech companies. Sometimes forget that they are other tech companies that need to follow the same laws. GDPR is a great start. For allowing people more options with their data. But it is flawed. With tech companies paying major fines. And appeals costing a fortune. A small tech company can't afford the lawyers that Facebook or Google has. Or even the money to pay the fine. This is the way some websites was blocked from EU users for a long time. As they knew that they can’t follow the GDPR guidelines to the tea.
Surrounding all the hype, self-driving vehicles may prove useful. But not in the way you think
I was watching an episode in the Bloomberg Quicktake series called Hello World. If you haven’t watched the series yet then start now.
In this episode, they talked about self-driving spraying vehicles. They were big contraptions. Which drove around a farm. And sprayed the plants. While the vehicle was moving along. The vehicle uses normal self-driving sensors. Like LIDAR and cameras and GPS. For extra comfort. It is a detailed map of the farm. Due to the automation of the device. One person can manage 5 of the vehicles using their laptop. If there is a problem the vehicle will send a notification to the device. So the human can go check up on the vehicle.
After the episode ended the did a quick review of the episode. And Ashlee talked about he didn’t believe something like that existed. He only known about it when some person on Instagram gave him information about the company.
An interesting mention about the encounter. Compared to Silicon Valley people. They were less braggadocious about how they made their product and their achievements. As they didn’t hype to the moon how much their device can do. The second is that they are selling these devices around the US. And soon internationally. This is fantastic compared to other self-driving cars. From the tech companies. Which sold close to none. With billions of dollars to play with.
So self-driving vehicles are useful not in the traditional way. Not in the driving you to grocery shop and back. But in the way collecting blueberries in a farm way. In the documentary.
In my opinion they did not talk much about the engineering feat of the vehicle. As they needed to create a vehicle. That sprayed on demand. And the engine that could power that. And all the other sensors on the device working together. That I think is better engineering work and the tech companies. As they stick a few cameras on the top of the car and call it a day. It showed in the video they manufacture the vehicle in house. The only tech companies that do that is Tesla. So this company is likely way ahead of many companies in silicon valley.
The only difference.
They don’t spend millions of dollars on hype and marketing. But innovating in their sector.
And only people in the agriculture space will know about it. Talking about agriculture technology. In the end interview. The host talked about she knew a friend that used robots to milk cows. She said the robot used lasers. And robot works how to milk the cow from there.
Ashlee gave another example. In which he knew a company in Idaho. Which had a robot collect rocks for them. This is important because of the sheer amount of land. Over time the ground will churn out rocks. So they need to remove the rocks from the land. So they can plant the crops properly.
From what I can see there is a lot of movement going on in the AgriTech space. Which they don’t generate the same amount of hype of a software silicon valley firm. But may do even more important stuff. Compared to an app that helps you get snacks to your house cheaper.
How video and social media effects public discourse.
Olden day speeches were serious
While reading more of Neil Postman’s book Amusing Ourselves to Death. He talked about the power of print media in American public life. Where conversations and speeches were done in a literary tone. A great example shown was the Abe Lincoln vs Douglas debate. Which lasted for 12 hours. And it was none of the speeches we saw today. As the whole debate sounded like an essay. Even the comebacks were pre-written. Postman noted that as the language was complex. Because the speakers assumed major assumptions with the audience. Which required the understanding of the political issues at the time. A couple of the jokes or statements made by the speakers would not be understood would the knowledge of the political context. Nowadays speeches are very simplified so they can turn into clips for television. If people did a speech in the 17th-century style. People will find you boring and would not know what you're talking about.
Can you understand a complex topic in less than 5 minutes?
With social media. I wonder how its effecting discourse right now. You have likely seen it on one of your feeds. A short video with subtitles at the bottom. Talking about whatever political topic. But while watching them you tend to notice that they omit a few details. Which may be important to the topic at hand. Within less than 3 minutes. This video is supposed to tell you how simple a complex political issue is. Only to tell you how obvious the solution is. As we can see the problem comes when the analysis is devoid of nuance. And most of the goal is to make you high on emotion.
I don’t know how much reasonable information you can pack into a 3-minute video. I guess people decide how much emotion they can pack into a video instead. Social media forces you to play more to a certain rule set. Incentivizing creators to play on people’s emotions more. Social media companies want engagement on their platform. This causes people to come back more often and stay longer. This means social media companies like emotional content. As it drives up engagement. So we have a forcing function pushing users for more emotional content. This is a far cry from the Lincoln debates. Also, video is easier to share. A person can watch the video and share it in less than 5 minutes. Compared to text, which it may take a while to read and comprehend.
Before video, you used to read the whole person’s argument. Now we only get 30-second clips of the incident. So at best we only get a surface-level understanding of the incident. At worst we come out of the situation misinformed. As we don’t have enough context to get a full understanding of the event. But after watching the video we are very confident about the situation. So we develop a strong Dunning-Kruger effect. So when talking to other people. We tend to be emotional when talking about the event. Because that’s how we got the information. And lack the context to understand the problem. So we tend to talk past each other.
The algorithms may be more powerful than the content
While I haven’t finished reading the whole book. Neil Postman’s does talk about the issue of television. And how its visual form takes priority above everything else. This can be same for social media videos. In which the visuals that entice users to click, matter more. This is why you can see insane thumbnails for videos. As they need to capture the attention of the user. Even along with the video, the person may be making emotionally charged statements. Because they still want to keep the user’s attention. And stop the user from clicking away. In TV at least you have broadcasting laws. But on the internet, social media companies give a wide birth. Which in a way is a good thing. But in other ways not so. As mass misinformation can be shared. Without much of a fact-check along the way. The emotionally charged nature of misinformation means people are willing to ignore fact-checking. And will actively discredit the correct information. As the correct information goes against their worldview.
I think most progress being done to stop misinformation on video. Is not the fact check panels on the bottom of the video. But adjustment of the algorithms themselves. For example, many of the social media companies now will slow down the distribution of the content. If it's getting viral and the content is misinformation. This stops it from reaching a large number of people. This is done in many ways. YouTube slowed down the distribution of conspiracy theory videos by stopping them from entering the suggested content panel. This means the video will find it difficult to find new users. Outside of the person’s subscriber base.
Adjustments to the social media algorithms also force creators to evolve to the changes. For a long time controversial topics (mainly current affairs). Got demonetised on YouTube. Meaning creators couldn’t run ads on the platform. So creators either started pivoting towards more family-friendly content. Or opening up Patreon accounts so people could get funded directly from their fans. This lead to creators creating exclusive content for Patreon. As they don’t need to worry about getting demonetised or banned from YouTube.
Facebook is known to do similar things. Like slowing down distribution of serious misinformation. This was being done for coronavirus information. Social media will rather do this. Because it’s much harder to cry censorship. While people cry about shadow banning. (stopping content from getting distribution as I explained above.) It's much harder to prove. And most of the time it’s just people’s content was not good enough for users to share it. That does not sound nice so people resort to the shadowban cope.
While Neil postman did not predict social media. I think his book is very relevant to us now. It’s not just the content affecting our discourse but the algorithms behind them as well.
The Uses and Need for Interpretable AI
A popular topic in the deep learning research space is interpretable AI. Which means AI that you can understand why it made a decision. This is a growing field as AI is creeping into lots of industries. And accuracy is not just important but the method or reasoning of the decision is important as well. This is important were the consequences of the decision is very critical like medical and law. In a Wired article, they highlighted this when the AI was flagging patients to check up. Which the doctors have no clue why the AI did that. So some doctors took it upon themselves to learn some stuff about the AI. To guess what it was thinking when it decided. Not having reasoning for the AI lead to some disagreements between the nurses and doctors. So having an explainable AI would help in that scenario.
Interpretable AI can help discover bias in the AI. When AI was used in the criminal law realm. Lots of AI tended to make harsher judgements of black people compared to white people. Having an explainable AI would have made it much clearer that the AI was being driven by the race of a person. Explainable AI can give us peace of mind if done correctly. As it can list the variables that affected the sentence of the person. Instead of a black box-like now where it just spits out a number. It could tell why it spat out the number.
I'm going, to be frank. I only have a surface-level understanding of the topic. I only read one or two light papers. So more reading will be needed. And also implementation. But I think interpretable AI can be very useful for many AI systems like I explained above.
One video I watched. Said her and her team used interpretable models to debug models. And was able to increase the accuracy of the model significantly. So we can be able to do stuff like that. Debugging deep learning models is hard. Due to the black-box nature of them. An interpretable model can help us shine the light on these models. Helping us improve our models even more. In an unreleased post, I wrote about interpretable AI can help make recommendation systems used by the major tech companies more transparent. Also leading to more understanding by users and other stakeholders like the regulators. This can help people identify harmful rabbit holes like conspiracy videos and anti-vax content.
By having a better understanding of why a tech service is recommending you stuff. The user can take action towards changing the situation or keeping it like it is. Maybe using that information the tech company can add features to stop a user from falling too deep in an echo chamber. Like adjusting the suggested videos to more moderate content. Or videos that have different views than the echo chamber that the user is in. Or maybe have nudges saying, “you been watching the same type of videos for a while, try something else.”
Also, it can help identify videos that are going viral in certain areas. Especially if the area is problematic. So, if you can see a video in conspiracy theory land. Gaining traction, you can see how and why the algorithm is recommending the video. From there the tech company can make the decision to do a circuit breaker with the video. Or let it run its course.[1] This may be better than trying to outright ban topics on your service. Due to the whack a mole effect.
Obviously, almost all of this is automated. So the insights are taken from the interpretable AI. Will need to be transferred into another system. And be factored into the model. I don’t know how would one implement that though.
An explainable AI can help moderation teams for tech companies. As an AI can help tell the moderators why it decided to ban a piece of content. And if its an appeal then the moderator can explain to the user why he was accidentally banned. And explain to the user how to avoid it from happening again. Also, the moderator can help tell the AI that it was wrong. So the AI can get better at its job next time around.
When YouTube videos get removed from the platform. YouTube does not tend to offer a good explanation of why it was so. It normally gives some PR / Legal email saying you violated terms and conditions. But the creators do not know which terms and conditions were violated. Some YouTube creators may resort to complaining on Twitter to get a response from the company. While I think YouTube is partly vague because of some legal situation. I think having a transparent AI can help. YouTube can show creators why the situation is like this. YouTube may not know what happened due to the black-box nature of the algorithm.
Interpretable AI will not solve all of technologies problems. A lot of problems frankly is a lack of government regulation and oversight. As in many areas of technology, there are no ground rules. So the technology companies are going in blind. And people are upset about whatever the tech companies do. If the legal situation changed were YouTube can tell its creators why it violated its terms and service. That will be great. Instead of having a cat and mouse game. If government officials even knew what it was talking about when it came to technology. Right now I think the European Union has the best understanding. While I think some of its initiatives are flawed. In the USA the government is only now waking up to the fact they need to rein in big tech. But I'm not confident that the government has a good understanding of the technology there dealing with. You can see some of the viral videos of the original Mark Zuckerberg hearings. Where the congressmen were asking questions that lacked a basic understanding of what the internet and Facebook even is. Never mind how should the government deal with data. Or incentivise companies to have transparent AI.
[1] Tech companies already do this to fight this misinformation. But an Interpretable can make this process easier.
Will building leverage get easier thanks to better technology?
While reading some of the work of Naval Ravikant. On the topic of building wealth, he stresses the need for someone to build leverage. Meaning your time is not correlated to your output. Meaning you can work on a product for 10 hours and the returns can be 10x or 100x the value. This is why Naval urges people to stop swapping time for money. Even doctors and lawyers get paid a lot by the hour. Won't get as rich. If they did. They started something separately like a private practice or selling a medical product.
Naval talked about the most recent form of leverage. Which are products with little marginal cost of replication. Which means media, books and code. With the internet, the cost of replication is close to zero. So if you write an eBook and sell it on amazon. You don’t need to pay the printing cost. It simply goes straight to the user. Naval explains this new form of leverage is great because it’s permission-less. You can simply start producing without another person’s approval. Things like social media, blogs and podcast also count in this bucket. All you need is a microphone or a camera and you can start. He mentioned that code can come with extra leverage because it can work 24/7. In this context he is talking about you can rent servers from the tech companies where you can place your code in. And can run the service for you 24/7.
Which brings me to the topic at hand. Code is a high leverage skill because of the possibilities you can make with it. With little replication cost. I'm thinking that while code is already high leverage skill. Machine learning may increase that leverage even more. As you are teaching a robot. Learn a process. Once that process is learnt it can be replicated to many places. Compared to normal programming where you are making the end product from scratch. Before you may have to rely on human leverage aka human labour[i]. But now you can use AI to complete a task. Which may be done faster and more accurately. Like what Naval said you have datacentres packed with robots. So you have other robots helping the robots you made. As the machine learning model improves from your use of the product. Due to users adding new data to the model. As time increases the leverage also increases as well.
I think the best examples of this are the major tech companies. With Google, each search is making the service better. As they collecting data on how the service is being used. As the service gets better more people are more likely to use the search engine as it gives them what they want. Expanding there reach even more. Same with Facebook. For the data, they collect. This is to make the service better. (which means more money). As you click on certain posts on your timeline you are training the algorithm. Which means it will show more posts that you’re more likely to click on. Helping you stay on the platform. As it knows more about you it can sell that information to advertisers. Were targeted ads can be displayed on your feed.
So compared to other products. Machine learning products. Can have a strong flywheel effect. Where the cost of replication is not just low but its better after each replication. This is where some of these algorithms get powerful. This is why regulation is likely going to step in. As the flywheels of the companies are just too strong. And Competitors can't compete with them. The competitor will never have enough data coming to go head to head with them. Don’t get me wrong they are some exceptions like Tick Tock. Where they got a great data flywheel going. Helping to grow the product even more.
Let’s go back to the ebook example. As soon as the author publishes the book. The author is not getting paid by the hour. Is getting paid by items sold. Disconnecting him from the input and the output. As the author can make tons of money by selling lots of items. While hours put into the book is the same. Which is where the leverage comes from. But imagine each time a person gets a book. After they have finished reading it. The book improves ever so slightly. So when the next person comes then he is seeing an improved book. This is what is happening for the tech companies that I mentioned above. So it will be hard for a beginner to catch up.
While these systems are democratic meaning everyone can use them. After a while, they become almost an oligopoly. Due to entrenched powers. This can be applied to the highest levels like the tech companies. To the lower levels like the content creators. A content creator nowadays will find it harder to build an audience than a couple of years ago. Due to massive content creators on the platform taking attention from most users. As they pull lots of the clicks and views. The algorithms tend to have a bias to favour them. Due to the history of generating attention for their platform.
Therefore making an entrenched class of content creators on a platform. Those content creators can use the money they earned from making content. To make better content that people are more likely to see. (good on them though). And leveraging their audience to help get further reach. Some of these issues are only a result of content creators leveraging their audience for greater heights in their career. Which is great for them don’t get me wrong. But because of the increased leverage. The compounding interest makes it harder for everyone to catch up.
This is not a sob story. With the internet now all of us have the chance. To make our flywheel. And what naval said you can “Escape Competition through authenticity”. Meaning you can make your monopoly just by being yourself.
[i] A lot of human labour is still used for labelling data. So human labour is still important for making AI.
Remote work will get better because of human behaviour, not software
My experience
Right now, I have been doing university lectures for a few months now. With that, I have been in a few video calls. Like many people during the pandemic. When taking video calls I’m starting to notice that adapting to remote work. Is mostly related to dealing with human behaviour, not software.
For example, in my video calls. Almost all the students don’t use our cameras. And many people use the chat. To communicate with each other and the teacher. This is very different from many places. I know companies that all their employees. Have their camera on. And predominately use the microphone to speak.
The Need for Structured Communication
So even with the same software. People are using the software very differently. This has to do with remote work in general. Not just video calls. I had to do some group work. While it was good. The work was done over an ad-hoc fashion over WhatsApp. This means I was always on tab. Ready to read a message in the group chat. One time I was eating my food and I had to interrupt it. So I can complete some work.
Now I understand the stuff I hear about in Cal Newport’s podcast. Talking about the danger of using instant messengers like slack and email. For work communication. And the need to have more structured communication. There is popular software for project management, Like Notion and Asana. But its just matter of getting people to use them. As the status quo is easier, in the moment. Moving to a new system has a high activation cost. The team will need to ask important questions like, how to do transfer your tasks? How do you get everyone up and running with the software? It's lots of questions to answer.
New Systems of Working
As we are all new to this. People will eventually work out new systems. To get the most out of remote work. Some companies can use remote work to their advantage. Gumroad and other small tech companies. Can use asynchronous communication. Which means people don’t need to be at the same time or place to receive or send messages.
Sahil Lavingia, the founder of gumroad mentions that asynchronous communication:
All communication is thoughtful. Because nothing is urgent (unless the site is down), comments are made after mindful processing and never in real-time. There's no drama
Because everyone is always effectively "blocked," everyone plans ahead. It also means anyone can disappear for an hour, a day, or a week and not feel like they are holding the company back. Even me!
I think this is a thing of times to come. Because it is creating new working styles when remote work is possible. With this text-based style. No meetings are needed. And plans are well thought through.
Sahil also mentions that:
People build their work around their life, not the other way around. This is especially great for new parents, but everyone benefits from being able to structure their days to maximize their happiness and productivity.
This allows us to spend more time on things we love like our family or other hobbies. Remote work can make living without following the traditional 9-5 structure. Or the Hustle minded 80-hour workweek. Also, remote work people be more location independent. In many cities around the world. House prices are skyrocketing. Due to a lack of housing. People buy shoeboxes going for half a million dollars. Now with that same money, they can buy a whole acreage in Nebraska. Allowing them to have more space for their family and themselves. And depending on their lifestyle to have a more enjoyable time.
I remember many times in college. I wondered why I’m I need to the classroom. A lot of this work can be done at home. I guess a lot of people in the workplace think about the same thing.
With Asynchronous communication and remote work. We can allow employees to become time and location independent. The time independence within reason. Employees still need to get work done, that goes without saying.
Software is still very helpful
Even software itself can help people transition into remote work easier. NVIDIA has showcased awesome technology using GANs. That creates an artificial version of a person's face. Using key points on a person’s face. Allowing a person to use a video feed. With little bandwidth. So people in rural areas. And other places with a bad internet connection. Can join video calls. With that, they can fully be location Independent. As location independence implies that you have a good internet connection. So choppy video will become a thing of the past.
Like mentioned earlier they are a lot of project management tools out there. People just need to use them. People will need to get used to structured communication. People will need to get used using video calls. People are quickly designing etiquette on the fly. Like muting your microphone when you enter the call. Messaging before wanting to speak. Some companies have a culture of using video calls to get closer to the team. So employees will talk briefly about their lives once a week. In a hands-on meeting. And used it to get updates with the rest of the team.
The Potential of remote work
With remote work, people can have time to take walks around the neighbourhood or maybe cook lunch for themselves. Not just staying in an office all day. Granted people still need to get used to this as lots people including myself stay at home all day. Like I mentioned before it's not a problem of software but human behaviour.
So, employees on a personal level will start to get the most out of remote work by improving their overall being and productivity. During the summertime, I was able to take walks almost every single day enjoying the local scenery and parks (now it's winter is looking a bit more difficult.) On NHK world I watching a program talking about people moving to the suburbs. In which the town, Atami. Had a nice beachfront. And people wanted to move away from the hustle and bustle life of Tokyo. I can now imagine with remote work during your lunch break you can take walks along the beach and have seafood for lunch.
I think remote work is something that going to stay. There are lots of improvements still need to be made.
Deep learning is already mainstream
While I was scrolling on Twitter. I saw a tweet showing the word “deep learning” is plateauing on google trends. Then Yann LeCun replied that it's simply deep learning because become more normal.
This reminds me of a previous blog post that I wrote. Talking about how good tech is like good design. Meaning when technology is good. It embeds itself in our society and becomes invisible. Gmail’s smart compose feature is 100% deep learning. But we don’t think about it as ML. Amazon’s recommendations are ML. But we don’t think about them that way. In normal discourse, we just simply call them algorithms. Which is an accurate term. While abstracting away most of the advanced details.
This makes sense, as only nerds care about the type of algorithm. To recommend films on Netflix. Its recommendation systems. Everybody else will simply just say technology. Or treat the company as a person. Like “Netflix sent me this message.” “Facebook showed me this message.” Etc. When aeroplanes started getting popular we just said we took a flight to Washington DC. Rather than a mechanical flying device helped travel a couple of miles.
As I write this blog post. I use Microsoft Word’s read-aloud feature. To proofread the blog post. Where a robot voice reads why to work for me. The voice has improved tremendously. While it still has some robotic feels to it. It does a good job. It’s like an editor is personally reading my work. Also, I use the program Grammarly. While they do not say it I’m pretty certain they use machine learning. To spot mistakes in your work. These very useful tools that help me improve my writing are drive-by machine learning. Even though people will simply just call it technology.
This is the cycle of all technologies. You have hype. Depending on how good the technology is. It fails to even go into the mainstream. And start again in the hype cycle. If it's good. It will fall below expectations not because it’s bad. But failed to meet the sky-high expectations. Afterwards, people start to work out more practical uses of the technology. After a while, the technology gets popular. But lots of the hype starts to fade away. As people get used to the technology. So I guess deep learning or machine learning. The hype is starting to disappear, but people are finding uses for the technology.
You will have some standouts like GPT-3 and GANs. But most machine learning in the wild right now is a little bit boring. Recommendation systems. Think of Netflix and Amazon. Forecasting. Using past data. To predict future behaviours. It tends to be boring as its simply showing other data points based on past data. Or in amazon cases using the AI to help sell more products. Which is no surprise if your for-profit company. You need to make money.
While ML has its limits. I still think is very popular because it can do so many things. Like generating image via GANs. Classifying images with CNNs. To predicting past behaviour using forecasting. I think this is why AI is very popular. Because if you have some type of data. Which in the internet age, the answer is always a yes. Like early computers where it efficiently changed every industry either via automation or communication. With machine learning. It can help with those areas even more.
In the good tech is like good design blog post. I talked about technology tends to be popular when people stop noticing it. Which is happening now.
In the article I said:
no one calls their company “Excel-based” or “Windows-based”. As it’s [just] a tool.
When people started using office services on their computers. It was revolutionary at the time. But people now, don’t call themselves an “Excel-first company” Or an “email first company”. As people got used to them, people assume that using these services is a given. Soon having some type of data science role will be a given. Just like having a web developer for your company is a given. This will still mainly be focused on tech companies. But non-tech companies are not far behind. Non-tech companies hire web developers and server managers.
Battery Innovation
Recently I watched a few videos about Tesla battery day. Their annual shareholder meeting. Where Elon and his team talked about the improvements, they found in making batteries. This is a big deal. As making batteries cheaper and more efficient is one of the biggest bottlenecks facing Tesla. A lot of their improvement come from redoing their manufacturing process. And finding better minerals to use for battery manufacture. They touted a 69% reduction in cost per kilowatt per hour.
In my opinion the most important thing, Tesla announced was a cheaper electric car. With the price range around $25,000. As this should allow most people in the western world to buy an electric car. And move away from gasoline cars. Making the transition to cleaner transport faster. Because there is a still untapped market of lower-end electric cars. While they are some like the Nissan Leaf. And Toyota Prius. I think tesla can make a big play. And help most people purchase an electric car. Further removing our need for fossil fuels.
Making a good electric car is no minor feat. One the technological innovation side. So, trying to manufacture batteries that are cheap and efficient. So good enough to last for a long time in a car. The second on the supply chain side. By sourcing materials. And the chemical processes to get the materials ready for manufacturing and production. Which is why Elon mentioned that he wants the company to have an advantage with manufacturing batteries. Any car company that wants to create efficient batteries will need to do a lot of catching up to do with tesla.
Also, if western companies don’t want to get hammered on human rights issues. Then they need to find ways to source materials in the western countries. Tesla says they will be getting more materials from Nevada. So, I guess other companies will have to do the same. As I don’t know the mining locations cobalt or any other rare earth materials. I can’t give precise locations, for mining these materials. I guess European companies will do something similar by finding materials within European borders. A lot of innovation when it comes to batteries, I can see are not sexy. They are boring like finding better mining locations. Improving the chemical processes to make the materials more efficient. But this is the innovation of what is needed to transition to a carbon-neutral world. They will be some issues if companies want to mine in the west. First, companies need to make sure the mining does not cause a raucous with local homeowners. NIMBYism can derail the whole project. As mining practices need to be environmentally friendly to hit the same problems of open mining coal.
Mining will need to be environmentally friendly. To avoid problems that you have from mining something like coal. Like bad local air quality and destroying the local environment.