Feel like we’ve got a lot of tech savvy people here seems like a good place to ask. Basically as a dumb guy that reads the news it seems like everyone that lost their mind (and savings) on crypto just pivoted to AI. In addition to that you’ve got all these people invested in AI companies running around with flashlights under their chins like “bro this is so scary how good we made this thing”. Seems like bullshit.
I’ve seen people generating bits of programming with it which seems useful but idk man. Coming from CNC I don’t think I’d just send it with some chatgpt code. Is it all hype? Is there something actually useful under there?
I don’t think the comparison with crypto is fair.
People are actually using these models in their daily lives.
People have actually used crypto to make payments. Crypto is valuable, but only when it’s widely adopted. Before you say something like “use a database,” you might take the time to understand what decentralized blockchains are accomplishing and namely removing a class of corruption from any information coordination tasks.
Why bother with the overhead of blockchain when users centralise on a handful of
banksexchanges.Exchanges only exist to convert away from the crypto. If that’s the standard money, they don’t live. They aren’t the banks of the blockchain. They are the intersection of fiat banks and the blockchain.
Strongly disagree, some exchanges don’t even have fiat on-ramps.
Blockchain is inefficient and pointless when users centralise on coinbase and binance.
I love revisiting comments like these every 4 years.
And yet, people still don’t use crypto in their daily lives. How many years has it been?
Reddit just tied karma to the blockchain lol
Not saying it’s a good use, but lots of people are going to be using it now.
Have we forgot already about the entire country of El Salvador?
You mean, the one where people immediately exchanged their free crypto for USD as soon as they got it?
Do you know of another El Salvador?
Senior developer here. It is hard to overstate just how useful AI has been for me.
It’s like having a junior programmer on standby that I can send small tasks to–and just like the junior developer I have to review it and send it back with a clarification or comment about something that needs to be corrected. The difference is instead of making a ticket for a junior dev and waiting 3 days for it to come back, just to need corrections and wait another 3 days–I get it back in seconds.
Like most things, it’s not as bad as some people say, and it’s not the miracle others say.
This current generation was such a leap forward from previous AI’s in terms of usefulness, that I think a lot of people were looking to the future with that current rate of gains–which can be scary. But it turns out that’s not what happened. We got a big leap and now are back at a plateau again. Which honestly is a good thing, I think. This gives the world time to slowly adjust.
As far as similarities with crypto. Like crypto there are some ventures out there just slapping the word AI on something and calling it novel. This didn’t work for crypto and likely won’t work for AI. But unlike crypto there is actually real value being derived from AI right now, not some wild claims of a blockchain is the right DB for everything–which it was obviously not, and most people could see that, but hey investors are spending money so lets get some of it kind of mentality.
Same. 5 minutes after installing Copilot I literally said out loud, “Well… I’m never turning this off.”
It’s one of the nicest software releases in years. And it’s instantly useful too… No real adjustment period at all.
I tried it for a couple months and it was alright but eventually it got too frustrating. I did love how well it did some really repetitive things. But rarely did it actually get anything complex 100% right. In computing, “almost right” is wrong. But because it was so close, it was hard to spot the mistakes.
There were cases where my IDE knew the right answer but Copilot did not. Realizing that Copilot was messing up my IDE enhancements to produce code I was painfully babysitting, I cancelled it.
This is the most insidious conundrum related to AI usage. At the end of the day, a LLM’s top priority is to ensure that your question is answered in a way that satisfies that model. The accuracy of its answers are a secondary concern. If forced to choose between making up BS so it can have a response that looks right versus admitting it doesn’t have enough information to answer, it can and often will choose the former. Thus the “hallucination” problem was born.
The chance of getting your answer lightly sprinkled with made up stuff is disturbingly high. This transfers the cognitive load of the AI user from “what is the answer” to “I must repeatedly go verify everything in this answer because I can’t trust it”.
Not an insurmountable obstacle, and they will likely solve it sooner rather than later, but AI right now is arguably the perfect extension of the modern internet - take absolutely everything you read with at least a grain of salt… and keep a pile of salt cubes close by.
I’ve been a web developer for 22 years. For the last 13 years I’ve been working self employed from home. I cannot express how useful AI has become. As a lone wolf, where most of my job is problem solving, having an AI that can help troubleshoot issues has been hugely useful.
It also functions as a junior developer, doing the grunt programming work.
I also run a bunch of e-commerce sites around the world and I use it for content generation, SEO, business plans, marketing strategies and multi-lingual customer support.
It’s really good at filling in gaps, or rearranging things, or aggregating data or finding patterns.
So if you need gaps filled, things rearranged, data aggregated or patterns found: AI is useful.
And that’s just what this one, dumb guy knows. Someone smarter can probably provide way more uses.
Hi academic here,
I research AI - better referred to as Machine Learning (ML) since it does away with the hype and more accurately describes what’s happening - and I can provide an overview of the three main types:
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Supervised Learning: Predicting the correct output for an input. Trained from known examples. E.g: “Here are 500 correctly labelled pictures of cats and dogs, now tell me if this picture is a cat or a dog?”. Other examples include facial recognition and numeric prediction tasks, like predicting today’s expected profit or stock price based on historic data.
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Unsupervised Learning: Identifying patterns and structures in data. Trained on unlabelled data. E.g: “Here are a bunch of customer profiles, group them by similarity however makes most sense to you”. This can be used for targeted advertising. Another example is generative AI such as ChatGPT or DALLE: “Here’s a bunch of prompt-responses/captioned-images, identify the underlying way of creating the response/image from the prompt/image.
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Reinforcement Learning: Decision making to maximise a reward signal. Trained through trial and error. E.g: “Control this robot to stand where I want, the reward is negative every second you’re not there, and very negative whenever you fall over. A positive reward is given whilst you are in the target location.” Other examples including playing board games or video games, or selecting content for people to watch/read/look-at to maximise their time spent using an app.
What do you think on calling it AI?
So typically there are 4 main competing interpretations of what AI is:
- Acting like a human
- Thinking like a human
- Acting rationally
- Thinking rationally
These are from Norvig’s “AI: A Modern Approach”.
Alan Turing’s “Turing Test” tests whether a given agent is artificially intelligent (according to definition #1). The test involves a human conversing with the agent via text messages, and deciding whether the agent is human or not. Large language models, a form of machine learning, can produce chatbot agents which pass this test. Instances of GPT4 prompted sufficiently to text an assessor for example. The assessor occasionally interacts with humans so they are kept sufficiently uncertain.
By this point, I think that machine learning in the form of an LLM can achieve artificial intelligence according to definition #1, but that isn’t what most non-tech non-academic people mean by AI.
The mainstream definition of AI is what we would call Artificial General Intelligence (AGI). This is an agent that meets a given one of Norvig’s criteria for AI across multiple scenarios and situations that they have never encountered before.
Many would argue that LLMs like GPT4 do not meet the criteria for AGI because they are not general enough, unable to learn to play an Atari game for example, or to learn an entirely unseen language to fluency.
This is the difference between an LLM and a fictional AGI like Glados or Skynet.
Additionally forms of machine learning exist like k-means clustering, which identify related groups within a dataset as their only function. I would assert these are not AI, although a weak argument could be made that they are thinking “rationally” enough to meet definition #4.
Then there are forms of AI which are not machine learning, such as heuristic agents - agents that are hard coding with reasoning by humans - such as the chess playing Stockfish, or the AI found in most video games.
Ultimately AI can describe machine learning if “AI” is understood as something which meets one or more of Norvig’s definitions. But since most people say AI when they mean AGI, I think “machine learning” is a better term. Less undeserved hype, less marketing disinformation, and generally better at communicating what is being talked about.
Thanks for taking your time and putting it in that laconic way.
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It’s not bullshit. It routinely does stuff we thought might not happen this century. The trick is we don’t understand how. At all. We know enough to build it and from there it’s all a magical blackbox. For this reason it’s hard to be certain if it will get even better, although there’s no reason it couldn’t.
Coming from CNC I don’t think I’d just send it with some chatgpt code.
That goes back to the “not knowing how it works” thing. ChatGPT predicts the next token, and has learned other things in order to do it better. There’s no obvious way to force it to care if it’s output is right or just right-looking, though. Until we solve that problem somehow, it’s more of an assistant for someone who can read and understand what it puts out. Kind of like a calculator but for language.
Honestly crypto wasn’t totally either. It was a marginally useful idea that turned into a Beanie-Babies-like craze. If you want to buy or sell illegal stuff (which could be bad or could be something like forbidden information on democracy) it’s still king.
There’s no obvious way to force it to care if it’s output is right or just right-looking, though
Putting some expert system in front of LLMs seems to be working pretty well. Basically modeling how a human agent would interact with it.
We’ll see how that goes, I guess. I’m not involved enough to comment.
I’m guessing the expert system would be a classical algorithm?
AI is nothing like cryptocurrency. Cryptocurrencies didn’t solve any problems. We already use digital currencies and they’re very convenient.
AI has solved many problems we couldn’t solve before and it’s still new. I don’t doubt that AI will change the world. I believe 20 years from now, our society will be as dependent on AI as it is on the internet.
I have personally used it to automate some Excel stuff I do at work. I just described my sheet and what I wanted done and it gave me a block of code that did it. I had spent time previously looking stuff up on forums with no luck. My issue was too specific to my work that nobody seemed to have run into it before. One query to ChatGTP solved my issue perfectly in seconds, and that’s just a new online tool in its infancy.
For me personally cryptocurrencies solve the problem of Russian money not being accepted anywhere because of one old megalomaniacal moron
Cryptocurrencies didn’t solve any problems
Well XMR solved one problem, but yeah the rest are just gambling with extra steps
What problem is that? Genuinely asking.
Traceability.
Regular financial transfers, be they credit card, direct debit, straight-up written cheques, Interac/E-transfer (I am Canadian, that’s an us thing) are all inherently tracable.
XMR/Monero is not tracable, it’s specifically designed not to be, unlike Bitcoin and most other cryptocurrencies.
Of course, shitheads consider that to be a problem, but fuck them, they’re shitheads; it’s a solution, to the problem they cause.
For context, I say all this as someone who is vehemently opposed to prohibition; as far as I’m concerned every person who works for the DEA should be imprisoned or shot
Thanks for the info. That’s quite the way to end a comment though.
I mean it though.
The people working for the DEA now are no better than the people working to enforce alcohol prohibition in 1919. It’d be nice if humanity would learn, with a hundred years to think about it, but the ruling class at least haven’t. They enforce poorly thought out puritanical laws, and the world would be better off without them.
If I lived in America rather than Canada, which thank god I don’t, the DEA would happily kick down my door, shoot me, and then probably also shoot my wife, who doesn’t even partake of anything beyond alcohol, but would obviously be upset about my being shot.
All cops are bastards, and should be torched with molotovs at any available opportunity. If they didn’t want to be bastards, they shouldn’t have signed up as cops; it’s not like they’re conscripts
As a software engineer, I think it is beyond overhyped. I have seen it used once in my day job before it was banned. In that case, it hallucinated a function in a library that didn’t exist outside of feature requests and based its entire solution around it. It can not replace programmers or creatives and produce consistently equal quality.
I think it’s also extremely disingenuous for Large Language Models to be billed as “AI”. They do not work like human cognition and are basically just plagiarism engines. They can assemble impressive stuff at a rapid speed but are incapable of completely novel “ideas” - everything that they output is built from a statistical model of existing data.
If the hallucination problem could be solved in a local dataset, I could see LLMs as a great tool for interacting with databases and documentation (for a fictional example, see: VIs in Mass Effect). As it is now, however, I feel that it’s little more than an impressive parlor trick - one with a lot of future potential that is being almost completely ignored in favor of bludgeoning labor, worsening the human experience, and increasing wealth inequality.
They can assemble impressive stuff at a rapid speed but are incapable of completely novel “ideas” - everything that they output is built from a statistical model of existing data.
You just described basically 99.999% of humans as well. If you are arguing for general human intelligence, I’m on board. If you are trying to say humans are somehow different than AI, you have NFC what you are doing.
I think we’re on a very similar page. I’m not meaning that human intelligence is in a different category than potential artificial intelligence or somehow impossible to approximate or achieve (we’re just evolutionarily-designed, replicating meat-computers). I’m meaning that LLMs are not intelligent and do not comprehend their inputs or datasets but statistically model them (there is an important and significant difference). It would make sense to me that they could play a role in development of AI but, by themselves, they are not AI any more than PCRE is a programming language.
Don’t ask LLMs about how to do something in power shell because there’s a good chance it will tell you to use a module or function that just doesn’t plain exist. I did use an outline ChatGPT created for a policy document and it did a pretty good job. And if you give it a compsci 100 level task or usually can output functional code faster than I can type.
As a non-software engineer, it’s basically magic for programming. Can it handle your workload? Probably not based on your comment. I have, however, coaxed it to write several functional web applications and APIs. I’m sure you can do better, but it’s very empowering for someone that doesn’t have the same level of knowledge.
You have not realised yet that… yes, it has all the right to be called AI. They are doing the same thing we do. Learn and then create thoughts based on those learnings.
I even asked them to make up words that are not related to any language, and they create them, entirely new, never-used words, that are not even composites of others. These are creative machines. They might fail at answering some questions, but that is partially why we call it Artificial Intelligence. It’s not saying that it is a machine of truth. Just a machine that “learns” and “knows”. Sometimes correctly, sometimes wrong. Just like us.
Incorrect. An LLM COULD be a part of a system that implements AI but, itself, possesses no intelligence. Claiming otherwise is akin to claiming that the Pythagorean theorem is an AI because it “understands” geometry. Neither actually understands the data that they are fed but, are good at producing results that make it seem that way.
Human cognition does not work that way; it is much more complex and squishy. Association of current experiences with remembered experiences is only a fraction of what is going on in a brain related to cognition.
I am not saying it works exactly like humans inside of the black box. I just say it works. It learns and then creates thoughts. And it works.
You talk about how human cognition is more complex and squishy, but nobody really knows how it truly works inside.
All I see is the same kind of blackbox. A kid trying many, many times to stand up, or to say “papa”, until it somehow works, and now the pathway is setup in the brain.
Obviously ChatGPT is just dealing with text. But does it make it NOT intelligent? I think it makes it very text-intelligent. Just add together all the AI pieces we are building and you got yourself a general AI that will do anything we do.
Yeah, maybe it does not work like our brain. But is a human brain structure the only possible structure for intelligence? I don’t think so.
It does not create “thoughts”, it is very good at tricking humans into believing that it does, though.
You talk about how human cognition is more complex and squishy, but nobody really knows how it truly works inside.
It is not that there is no understanding, but rather that we have incomplete understanding. We know, for example, that human cognition is not purely storing recorded stimuli and performing associative analysis against them when meeting other stimuli.
All I see is the same kind of blackbox. A kid trying many, many times to stand up, or to say “papa”, until it somehow works, and now the pathway is setup in the brain.
This is a bit of a logical fallacy here, unfortunately, specifically false equivalency (ie. Thing A and Thing B both have characteristic C, therefore Thing A and Thing B are the same). This is exactly the sort of “dangerous” fallacy that a number of AI academics have warned about as well. LLMs are great at producing outputs that our socially-oriented brains can interpret as sentient thought and mistakenly anthropomorphize.
However, LLMs, as the word “model” in the name suggests, are statistical modeling software. They do not understand context or abstract meaning; only statistical occurrence of data in their stack, compared to the inputs. They are physically incapable of developing the Theory of the Mind due to the limitations in how they work.
But does it make it NOT intelligent?
No. The fact that they literally cannot actually understand anything or undertake contemplative, abstract thoughts is what makes them not intelligent. They do not understand the meaning of language; it is just data to them that has no context but how it relates to other parts of language.
Yeah, maybe it does not work like our brain.
I absolutely think that LLMs could be a component in AI but, alone, they are just like saying that a tire is a car because both can travel linear distances using rotation movements. By themselves, LLMs fail to fulfill what we tend to define as intelligence.
But is a human brain structure the only possible structure for intelligence? I don’t think so.
I certainly hope that the human brain isn’t the only possible structure for intelligence and find it very unlikely because our meat-computers aren’t really that special, even if we can’t entirely understand how they work yet (we’ve only really been trying for a relatively short time, compared to our species’ existence). We seem to agree there. I absolutely want AI as well as other non-human intelligence to be a thing because the idea of a universe in which humanity is the only sentience is very lonely and sad to me.
If you consider the amount of text an LLM has to consume to replicate something approaching human like language you have to appreciate there is something else going on with our cognition. LLM’s give responses that make statistical sense but humans can actually understand why one arrangement of words might not make sense over the other.
Yes, it’s inefficient… and OpenAI and Google are losing exactly because of that.
There’s open source models already out there that are rivaling ChatGPT and that you can train on your 10 year-old laptop in a day.
And this is just the beggining.
Also… maybe we should check how many words of exposure a kid gets throughout their life to get to the point to develop arguments such as ChatGPT’s… because the thing is that… ChatGPT does know way more about many things than any human being will ever do. Like, easily thousands of times more.
And this is just the beggining.
Absolutely agreed, so long as protections are put in place to defang it as a weapon against labor (if few have leisure time or income to support tech development, I see great danger of stagnation). LLMs do clearly seem an important part in advancing towards real AI.
Yes, it is useful. I use ChatGPT heavily for:
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Brainstorming meal plans for the week given x, y, and z requirements
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Brainstorming solutions to abstract problems
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Helping me break down complex tasks into smaller, more achievable tasks.
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Helping me brainstorm programming solutions. This is a big one, I’m a junior dev and I sometimes encounter problems that aren’t easily google-able. For example, ChatGPT helped me find the python moto library for intercepting and testing the boto AWS calls in my code. It’s also been great for debugging hand-coded JSON and generating boilerplate. I’ve also used it to streamline unit test writing and documentation.
By far it’s best utility (imo) is quickly filling in broad strokes knowledge gaps as a kind of interactive textbook. I’m using it to accelerate my Rust learning, and it’s great. I have EMT co-workers going to paramedic school that use it to practice their paramedic curriculum. A close second in terms of usefulness is that it’s like the world’s smartest regex, and it’s capable of very quickly parsing large texts or documents and providing useful output.
The brainstorming is where its at. Telling ChatGPT to just do something is boring. Chatting with it about your problem and having a conversation about the issue you’re having? Hell yes.
I’m a dungeon master and I use it for help world building and its exceptional.
I actually think that ChatGPT could eventually become the way to play tabletop RPGs. It’s not quite there yet, though. It’s not the most creative writer, still often has internal consistency flaws, and of course it would have to be trained specifically on the rules of the RPG you’re playing. But once it has been, it could probably act as a DM for groups that lack one. Or as a very closely coupled assistant to less experienced DMs who may need hand holding. It could even likely replace players, which could be useful for solo players who can’t find a group (or, say, have incompatible scheduling).
Unlike a regular video game, the format of tabletop RPGs seems perfect for our current rudimentary AIs and the constraints are ones that they can probably handle with careful training alone. It’s also a useful niche since there’s no replacing the open endedness of tabletop RPGs with current technology. There’s also a lot of people out there that I’m sure would like to play tabletop RPGs but just lack a group. Anyone who’s played them before knows that scheduling is really hard and has killed a lot of groups. That’s something an AI could help with.
I’m a dungeon master and I use it for help world building and its exceptional.
Oh that sounds neat. Can you give some examples of your process and results?
Honestly, not really. It’s a communication thing with the bot. Just talk to it like a person. Say what you want to do and what ideas you have, then ask if ChatGPT has any suggestions. Keep talking. It’ll recommend ideas and you can tweak them or ignore them.
When talking about code though I’ve come to notice that it will happily follow the corrections you tell it whether they are right or wrong. That’s not all that helpful but it can still give you ideas about how to solve your problem with a bit of basic knowledge of the topic you’re dealing with.
This. ChatGPT strength is super specific answers of things or broad strokes. I use it for programming and I always use it for “how can I do XYZ” or “write me a function using X library to do Y with Z documentation”. It’s more useful for automating the busy work
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Yes. What a strange question…as if hivemind fads are somehow relevant to the merits of a technology.
There are plenty of useful, novel applications for AI just like there are PLENTY of useful, novel applications for crypto. Just because the hivemind has turned to a new fad in technology doesn’t mean that actual, intelligent people just stop using these novel technologies. There are legitimate use-cases for both AI and crypto. Degenerate gamblers and Do Kwan/SBF just caused a pendulum swing on crypto…nothing changed about the technology. It’s just that the public has had their opinions shifted temporarily.
Focusing mostly on ChatGPT here as that is where the bulk of my experience is. Sometimes I’ll run into a question that I wouldn’t even know how best to Google it. I don’t know the terminology for it or something like that. For example, there is a specific type of connection used for lighting stands that looks like a plug but there is also a screw that you use to lock it in. I had no idea what to Google to even search for it to buy the adapter I needed.
I asked it again as I forgot what the answer was and I had deleted that ChatGPT conversation from my history, and asked it like this.
I have a light stand that at the top has a connector that looks like a plug. What is that connector called?
And it just told me it’s called a “spigot” or “stud” connection. Upon Googling it, that turned out to be correct, so I would know what to search for when it comes to searching for adapters. It also mentioned a few other related types of connections such as hot shoe and cold shoe connections, among others. They aren’t correct, but are very much related, and it told me as such.
To put it more succinctly, if you don’t know what to search for but have a general idea of the problem or question, it can take you 95% of the way there.
My concern is that it feels like using Google to confirm the truth of what ChatGPT tells you is becoming less and less reliable, as so many of the pages indexed by Google are themselves created by similar models. But I suppose as long as your search took you to a site where you could actually buy the thing, that’s okay.
Or at least, it is until fake shopping sites start inventing products based on ChatGPT output.
Now there’s a money-spinner!!
Please note: I’m not being serious
Man that’d be useful I’m actually struggling to find a really niche electrical connector roght now
Yes, community list: https://lemmy.intai.tech/post/2182
LLM’s are extremely flexible and capable encoding engines with emergent properties.
I wouldn’t bank on them “replacing all software” soon but they are quickly moving into areas where classic Turing code just would not scale easily, usually due to complexity/maintainance.
Nice list dude
I work at a small business and we use it to write out dumb social media post. I hated doing it before. Sometimes I’ll write it myself still and ask chatgpt to add all the relevant emojis. I also think ai had the chance to be what we’ve always wanted from Alexa, assistant, and Siri. Deep system integration with the os will allow it to actually do what we want it to do with way less restrictions. Also, try using chatgpts voice recognition in the app. It blows the one built into your phone out of the water.
So I’m a reasearcher in this field and you’re not wrong, there is a load of hype. So the area that’s been getting the most attention lately is specifically generative machine learning techniques. The techniques are not exactly new (some date back to the 80s/90s) and they aren’t actually that good at learning. By that I mean they need a lot of data and computation time to get good results. Two things that have gotten easier to access recently. However, it isn’t always a requirement to have such a complex system. Even Eliza, a chatbot was made back in 1966 has suprising similar to the responses of some therapy chatbots today without using any machine learning. You should try it and see for yourself, I’ve seen people fooled by it and the code is really simple. Also people think things like Kalman filters are “smart” but it’s just straightforward math so I guess the conclusion is people have biased opinions.
What regular people see as AI/ML is only a tip of an iceberg, that’s why it feels kind of useless. There are ML systems which design super strong yet lightweight geometries, there are systems which track legal documents of large companies making lawyers obsolete, heck even cameras in mobile phones today are hyper dependent on ML and AI. ChatGPT and image generators are just toys for consumers so that public can get slowly familiar with current tech.
I find it useful in a lot of ways. I think people try to over apply it though. For example, as a software engineer, I would absolutely not trust AI to write an entire app. However, it’s really good at generating “grunt work” code. API requests, unit tests, etc. Things that are well trodden, but change depending on the context.
I also find they’re pretty good at explaining and summarizing information. The chat interface is especially useful in this regard because I can ask follow up questions to drill down into something I don’t quite understand. Something that wouldn’t be possible with a Wikipedia article, for example. For important information, you should obviously check other sources, but you should do that regardless of whether the writer is a human or machine.
Basically, it’s good at that it’s for: taking a massive compendium of existing information and applying it to the context you give it. It’s not a problem solving engine or an artificial being.
I feel like it won’t be AI until we figure out how to point it back at itself, have it review its own answers and then be ‘happy’ when it’s answers are right. Not necessarily like if the user gives it a good score, but if it recognizes an answer it had given was actually used, or a prediction it makes if proved true (if I answer this way, the user is likely to ask this as its next question, etc) and it starts changing its behaviour, and asking itself questions to get better at that.
As a senior developer I see it unlocking so much more power in computing than a regular coder can muster.
There are literally cars in America driving around on their own, interacting with other traffic , navigating problems and junctions, following gestures and laws. It’s incredible and more impressive than chatgpt is. We are on our way to self-driving cars and lorries, self-service checkouts, delivery services and taxis, more efficient machines in agriculture and so many other things. It’s touching every facet of life.
we’re at a point where we’ve seen so many wonderful benefits of AI it’s time to apply it to everything and see what sticks.
Of course some people who invest in the stock market lose money but the technology is more than a step forward, it’s a leap forward.
Several autonomous car companies operate in my city. They’re impressive technology, but they’re not nearly as good as an attentive human driver. In particular, they have problems coping with anything unexpected, such as road closures or emergency vehicles, and they do not understand gestures.
