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A.I Will Kill 50% Of Jobs

GetShitDone

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Artificial Intelligence is predicted to wipe out 50% of all jobs. That goes for both blue collar and white collar.

A.I will replace low skill labour with robots and additionally replace medium (and even high) skill labour with machine learning software.

Mark Cuban has said that A.I will create the world's first Trillionare.

Jack Ma has said that it will create massive wealth disparity.

It's where all the mainstream billionaire's heads are at, all corporations are massively investing in it, and tech start ups are going towards it.

Has anyone thought of going into A.I to use to disrupt an industry? Thoughts and tips on it?
 

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cutthroughstatic

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If A.I wipes out most jobs, we can just all live on a beach somewhere while a sexy robot bring us mimosas. That's Fastlane, right?
 

Thoelk

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You don't just 'go into AI', there is a reason for it that the giants have big teams working on it... And that the most intelligent people are grouping together around the topic... Being a practioner myself, I follow Musk's opinion. But why is anyone surprised about jobs dissapearing? What happened when society got it's first machines? Howmany administrative jobs got lost (f.e. bank admins), while the IT sector boomed? There is a shortage of IT-skilled people though...

And nowaydays machine learning and especially AI and deep learning is overhyped... Ever single line of code in Python is declared as full-stack AI or whatever...

If anything, I see a big future into service/human interaction & empathy... Machines/Software will learn to reason in the most rational way possible, but human empathy is where we could & should shine... And the real question will be how to cope with the change of society, where jobs are lost & people get basic income.. How to spread & balance..
 

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By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.
 
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GetShitDone

GetShitDone

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If A.I wipes out most jobs, we can just all live on a beach somewhere while a sexy robot bring us mimosas. That's Fastlane, right?
That will be a small % of the population that decided to capitalise on A.I

The trick is for an entrepreneur to go for where the incumbents (Google, Microsoft, etc) AREN'T investing in A.I. They're going for the broader and larger consumer verticals.

However, an entrepreneur can go for a specific vertical in a niche or industry that is untapped or not as saturated and still make anywhere form 8 to 10 figures worth of value.
 

EvanOkanagan

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I think it's just as important to look at what you shouldn't invest your time/money in when looking at AI.

One example, parkades.

Parking space will become less and less needed due to AI (autonomous cars in this case). Car ownership in major cities in the future will become less common; rather, there will be thousands of self-driving cars that you can order and will come to you instantly. Think of how many cars just sit somewhere all day or night, not being used for any purpose-- it will be much more economical & convenient. Car ownership will be for the wealthy or those who live in more rural areas. And this will cause parkades to go out of business, or force them to change their use.
 

Thoelk

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However, an entrepreneur can go for a specific vertical in a niche or industry that is untapped or not as saturated and still make anywhere form 8 to 10 figures worth of value.
I think you're mixing up AI with machine learning algo's. That's happening the whole time already... If anything SaaS/AaaS is the way to go..
Don't fight a mammoth, let them create the agents.. as of now, we are nowhere near resourceful enough to create, leave alone sustain, AI agents...
 
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GetShitDone

GetShitDone

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I think you're mixing up AI with machine learning algo's. That's happening the whole time already... If anything SaaS/AaaS is the way to go..
Don't fight a mammoth, let them create the agents.. as of know, we are nowhere near resourceful enough to create, leave alone sustain, AI agents...
Thanks for that and your above post. You're right that AaaS is where I'm going. Do you think it's too late and too over-saturated to create AaaS given the amount of AaaS start ups? I see a new one getting funded everyday (eg. marketing, data analytics, chatbots, etc)

Are there specific industries still left untapped in your opinion?

Brutal honesty time. Use AI. Don't build it. You're out of your depth. See what Starbucks is doing? Build models like this with a consumer centric focus. Starbucks Is Preparing to Unleash Creepy New Artificial Intelligence That Sucks More Money From You
Very good point. It is definitely going to shape our user experiences for sure.

Perhaps that goes with @EvanOkanagan 's point of going into what A.I won't effect and using A.I tools to get ahead in a market that won't be disrupted by A.I itself.
 

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Thoelk

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Brutal honesty time. Use AI. Don't build it. You're out of your depth. See what Starbucks is doing? Build models like this with a consumer centric focus. Starbucks Is Preparing to Unleash Creepy New Artificial Intelligence That Sucks More Money From You
Exactly... As stated before, let the big guys do the 'dirty' work. Use their knowledge & result in our own beneficial way. Tbh, the link is a typical example of the press calling machine learning algo's AI... There is no agent involved, just a learning algorithm... But I guess the AI sounds better in an article! :)
 

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I think neural networks are fascinating.

Basically you have a ton of connected "neurons" in between source data and the output data that teaches itself how to get really damn good at things. You basically show it as much information as you can. Boatloads. The more the better.

Example: for facial recognition (like your phone and lots of other things use), you give the program a million photos. Photos of whatever. Doesn't matter. Then you ask it to show you a photo that has a face in it. It spits out a photo of grass. You then show it a proper photo of a face and feed that back into the program. It just learned how to get better at finding faces. Repeat this enough, and it gets DAMN good at finding faces.

You may have noticed google photos does this. If you use google photos online - you can literally google search your photos even if none of those photos are named or have any identifying data in them at all. I've typed in "brewing" and it shows me every photo I've ever taken of my homebrewing adventures. Or "cooking" and I get all my photos that have ingredients being prepared. It's amazing.

But again - you can't see how it does this. The learning network is too big. Too many connections. You just know that the more you train it, the better it gets, but how it's actually deciding what is and is not food is unknowable. Some folks are working on this but the term "black box" that's used to describe this hidden logic is pretty accurate for now.

Here's a fun quote on another application I just pulled from a recent science article:

In another study, the team predicted county-level heart disease mortality rates by analyzing 148 million tweets; words related to anger and negative relationships turned out to be risk factors. The predictions from social media matched actual mortality rates more closely than did predictions based on 10 leading risk factors, such as smoking and diabetes. The researchers have also used social media to predict personality, income, and political ideology, and to study hospital care, mystical experiences, and stereotypes. The team has even created a map coloring each U.S. county according to well-being, depression, trust, and five personality traits, as inferred from Twitter.


They fed the damn thing tweets and spat out heart attack stats that was more accurate than medical risk factors.

This is the same kind of thing that detects suspicious behaviors at airports and on security cameras.
It's how self driving cars are going to learn.


Getting back on point - if I were more technological, I'd be looking at building neural nets in highly valuable ways and focusing business activities on that.

Imagine the marketing applications!
Frankly I'm surprised that facebook ads (and all the rest) are still human operated at all. I really expect very shortly if you want to sell your widget you'll just tell the tool what you are selling, feed it as many variations of your ad as possible, put in a budget, and let it figure it out for you. I wouldn't be surprised if they start writing the headline for you as well.

This kind of thing gets me excited. It's kind of exciting knowing so many of them already exist as well but the future of this is going to get scary-good in very short order I suspect.
 

Tim Allen Jr.

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AI is a hugeeeee area. Knowing where to go with AI will be much more important in the foreseeable futures. With endless opportunities comes endless choices.... the tyranny of choice. While everyone is solving large problems with AI (in general machine learning, algos.... yadda), what are some simple areas that will need solved. An example of this is from silicon valley.... from jin yangs 'hotdog/not hotdog' analogy. While everyone tries to solve image recognition, there are simple applications that could make BANKKK.....

A lot of the revenue i generate is from solving a super simple problem that many many people overthink.
 

Thoelk

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But again - you can't see how it does this. The learning network is too big. Too many connections. You just know that the more you train it, the better it gets, but how it's actually deciding what is and is not food is unknowable. Some folks are working on this but the term "black box" that's used to describe this hidden logic is pretty accurate for now.

Getting back on point - if I were more technological, I'd be looking at building neural nets in highly valuable ways and focusing business activities on that.
Training a network too much doesn't make it necessarily better, as you can get overfitting & the fact that the neurons learn the training-data by heart.
I agree neural networks are super interesting & amazing (and the main reason I changed gears towards this field), but as I grow within this field I came to realize that NN are over-hyped at the moment. In that way, that more standard algorithms actually do the same classification job just as good as a NN... Yet the computational effort is way lower.

In my humble opinion neural networks hare the coconuts, high up in the trees... and there is still lots of other low-hanging fruit to discover. But NN's just sound too sexy to skip, so it gets most of the attention. But when you actually start with it, you realise that you need a) lots & lots of computational power b) lots & lots of time c) lots & lots of training data, compared to other algorithms.

I've found a nice article on the difference between Machine Learning & Artificial Intelligence. It sums up the difference quite ok I think. What we call AI at the moment is mainly ML, but AI is so much bigger.
Machine learning versus AI: what's the difference?

To answer the question on industries left untapped, I'd go even further. Everyone is focussing on the cool algorithms & the 'amazing' results that you get out of it. Yet while being in the industry, I've noticed that the data-industry itself mainly lacks & screams for people with skills to support all the ML/AI development. During my slowlane job I'm actually trying to shift my focus onto the back-end, heading towards Hadoop (big data dumps) & Spark (lots of live-stream data f.e. IoT output) development. These skills are highly in demand and it isn't sexy at all. Yet I see a nice opportunity to become a freelancer in this niche.

Because as the saying goes on the forum: when the goldrush is ongoing, sell shovels. Machine learning/AI development is the goldrush, but the environment to run all these big calculations is the shovel! :)
 

Gigi Rodgers

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Empathy will always trump any bot or A.I.

I don't know about the future, but as of now...yeah, don't market to people, talk to people.
Don't give advice, give strategy.
Go deeper, not wider.
 

Lex DeVille

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Owning A.I. will be a worthwhile investment in the looong looong term. When machines replace all jobs, those who own machines will hold the money. But by then human life will be very different from how it is now and there's a chance we won't see monetary systems anything like they exist today.

For now all of this is headcanon.

One useless pattern I've caught myself in over the last 2 years is worrying about where A.I. is headed and how it will change the world. Will all jobs be replaced in 20 years? Will A.I. wipe us off the face of the Earth? Will man merge with machine and live forever?

Funny thing is, it's easy to forget where where the world is right now. Living in the future is a fast track to missing opportunity in the present. Investing in A.I. might be worthwhile. But it's kind of a crapshoot since you can't even know if money will exist by the time you build something great.

❥ Where will the world be in 1-5 years from now?
❥ What can you do that will be valuable in that time?
❥ How can you create freedom and live how you want sooner than later?
❥ How can you position yourself now to take advantage when the future arrives?

(or will you still be pondering how to take advantage of the future when it gets here?)

From these questions you can draw tangible results and create a Fastlane lifestyle within the present that opens the doors to a Fastlane lifestyle in the future no matter where the world ends up.

If your goal is to have the most money in the world someday, I'd go for A.I. But if your goal is just to create something that fuels your Fastlane lifestyle and lets you be happy .. then there's probably faster, easier routes.
 

EvanOkanagan

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Empathy will always trump any bot or A.I.
I think you underestimate AI capabilities. Empathy is very likely to be a learnable trait from AI.

You have to understand that the longer AI interacts with humans, the more information it stores and the more it learns.

Take for example an AI psychiatrist. Sitting there with a client, they would be able to analyze such small things such as tone, speed, and even slight facial & eye movements, and then match them instantly with thousands (millions even?) of other recorded cases.

Then-- by analyzing responses and reactions from thousands/millions of previous patients, will be able to give the most pleasing (empathetic) response or diagnose within a very short period of time what is wrong with the patient.
 

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